Packages and custom functions
The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644
# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor)
library(metaAidR) # see a note above
library(orchaRd) # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans) # see a note above
library(kableExtra)
library(GGally)
library(cowplot)
library(grDevices) # reqired for using base and ggplots together
# Below is the custom function to calculate the lnRR
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5) {
lnRR <- log(Me/Mc) + 0.5 * ((aCV2e/Ne) - (aCV2c/Nc))
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) - 2 * rho * ((aCV2c * aCV2e)/sqrt(Nc * Ne))
data.frame(lnRR, var_lnRR)
}
# Mc: Concentration of PFAS of the raw (control) sample Nc: Sample size of the
# raw (control) sample Me: Concentration of PFAS of the cooked (experimental)
# sample Ne: Sample size of the cooked (experimental) sample aCV2c: Mean
# coefficient of variation of the raw (control) samples aCV2e: Mean coefficient
# of variation of the cooked (experimental) samplesData import and processing
Import and process raw data
Import raw data
raw_data <- read_sheet("https://docs.google.com/spreadsheets/d/1cbmYDfIc2dxHJxBaowojUZZkN31NW4sL_pHw0t9eTTU/edit#gid=477880397",
range = "Data_extraction_2", skip = 1, col_types = "ccncccccncncccccnncccnccnncncnccnnncncncccccccc") # Import raw dataProcess raw data
processed_data <- filter(raw_data, !PFAS_type == "PFOS_Total")
processed_data <- filter(processed_data, !Species_common == "Fish cake")
write.csv(processed_data, here("data", "Rawdata.csv"), row.names = F)Load processed data
processed_data <- read.csv(here("data", "Rawdata.csv"))
dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples
#### Ratio_liquid_fish with "0" for the dry cooking category
dat<-dat %>% mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category =="No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is "No liquid", otherwise keep the same value of Ratio_liquid_fish
# arrange(select(dat, Cooking_Category, Ratio_liquid_fish, Ratio_liquid_fish_0), Cooking_Category) # Checking everything is fine
kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F001 | Alves_2017 | 2017 | Portugal | E001 | Flounder | Platichthys flesus | vertebrate | marine fish | 7.430000 | PFOS | 8 | linear | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C001 | NA | 25 | 1 | ng/g | 24.0000000 | NA | 1.5280000 | sd | technical | 25 | 1 | 22.0000000 | NA | 1.5300000 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F001 | Alves_2017 | 2017 | Portugal | E002 | Mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C002 | NA | 25 | 1 | ng/g | 3.1000000 | NA | 0.2120000 | sd | technical | 25 | 1 | 2.9000000 | NA | 0.1410000 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E003 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFUnDA | 11 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 13.3018868 | NA | 0.0471698 | sd | technical | 25 | 1 | 4.1509434 | NA | 0.0943396 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E004 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFDoDA | 12 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 3.5731707 | NA | 0.0243902 | sd | technical | 25 | 1 | 3.2073171 | NA | 0.0243902 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E005 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFTrA | 13 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 6.5283019 | NA | 0.0754717 | sd | technical | 25 | 1 | 10.0377358 | NA | 0.0754717 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E006 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFTA | 14 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 1.3736842 | NA | 0.0157895 | sd | technical | 25 | 1 | 1.3315789 | NA | 0.0210526 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E007 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 0.6467391 | NA | 0.0054348 | sd | technical | 25 | 1 | 0.3016304 | NA | 0.0081522 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E008 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 25 | 1 | 0.0869767 | NA | 0.0130233 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E009 | European plaice | Pleuronectes platessa | vertebrate | marine fish | 8.700000 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C004 | NA | 25 | 1 | ng/g | 0.2472826 | NA | 0.0081522 | sd | technical | 25 | 1 | 0.2527174 | NA | 0.0054348 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E010 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.770000 | PFBA | 3 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | NA | 50 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 50 | 1 | 0.2083333 | NA | 0.0090909 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E011 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.770000 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | NA | 50 | 1 | ng/g | 0.0241860 | NA | 0.0074419 | sd | technical | 50 | 1 | 0.0250000 | <LOQ | NA | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | NA |
| F003 | Bhavsar_2014 | 2014 | Canada | E012 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0860000 | NA | 0.1350000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | ML | 0.0950000 | 0.1350000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E013 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1920000 | NA | 0.2660000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2660000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E014 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2340000 | NA | 0.2910000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.2910000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E015 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.1010000 | NA | 0.0950000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0950000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E016 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2590000 | NA | 0.2410000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2410000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E017 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0730000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0730000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E019 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.5600000 | NA | 18.0000000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 18.0000000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E020 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3970000 | NA | 0.4330000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.4330000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E021 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0020000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E022 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0100000 | NA | 0.0160000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0160000 | 0.1042160 |
| F003 | Bhavsar_2014 | 2014 | Canada | E023 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.1180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1180000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E024 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1900000 | NA | 0.2320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2320000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E025 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2560000 | NA | 0.3100000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.3100000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E026 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.1000000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0800000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E027 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2850000 | NA | 0.2340000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2340000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E028 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0710000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0710000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E030 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.4500000 | NA | 15.6300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 15.6300000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E031 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3920000 | NA | 0.3590000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.3590000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E032 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0030000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E033 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0140000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0220000 | 0.0033813 |
| F003 | Bhavsar_2014 | 2014 | Canada | E034 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0780000 | NA | 0.1140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1140000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E035 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1820000 | NA | 0.2220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2220000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E036 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2270000 | NA | 0.2550000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.2550000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E037 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.0960000 | NA | 0.0810000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0810000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E038 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2750000 | NA | 0.2160000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2160000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E039 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0670000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0670000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E041 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.0300000 | NA | 15.1900000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 15.1900000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E042 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3930000 | NA | 0.3690000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.3690000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E043 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0030000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E044 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0130000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0220000 | 0.1000364 |
| F003 | Bhavsar_2014 | 2014 | Canada | E045 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.0990000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0220000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E046 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5660000 | NA | 0.1380000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1380000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E047 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8040000 | NA | 0.1670000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1670000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E048 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.0960000 | NA | 0.3960000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.3960000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E049 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.7740000 | NA | 0.3320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.3320000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E050 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.1400000 | NA | 0.8740000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.8740000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E051 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.3410000 | NA | 0.3910000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.3910000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E052 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 30.5200000 | NA | 9.2540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 9.2540000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E053 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0840000 | NA | 0.5710000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.5710000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E054 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1050000 | NA | 0.0600000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0600000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E055 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C010 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1800000 | NA | 0.0840000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0840000 | 0.1114940 |
| F003 | Bhavsar_2014 | 2014 | Canada | E056 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.1050000 | NA | 0.0370000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0370000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E057 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5480000 | NA | 0.1210000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1210000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E058 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8480000 | NA | 0.1550000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1550000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E059 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.1080000 | NA | 0.4040000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.4040000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E060 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.8280000 | NA | 0.4180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.4180000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E061 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.1150000 | NA | 0.7690000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.7690000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E062 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.2910000 | NA | 0.3460000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.3460000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E063 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 28.3700000 | NA | 11.9900000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 11.9900000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E064 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0450000 | NA | 0.6230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.6230000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E065 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1170000 | NA | 0.0730000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0730000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E066 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1900000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0800000 | 0.0034647 |
| F003 | Bhavsar_2014 | 2014 | Canada | E067 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.1010000 | NA | 0.0350000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0350000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E068 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5690000 | NA | 0.1080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1080000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E069 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8300000 | NA | 0.1300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1300000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E070 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.0440000 | NA | 0.3560000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.3560000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E071 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.7460000 | NA | 0.2830000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.2830000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E072 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.0670000 | NA | 0.7540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.7540000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E073 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.3590000 | NA | 0.4280000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.4280000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E074 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 28.1100000 | NA | 10.9300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 10.9300000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E075 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0900000 | NA | 0.6180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.6180000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E076 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1060000 | NA | 0.0650000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0650000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E077 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1880000 | NA | 0.0750000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0750000 | 0.1099340 |
| F003 | Bhavsar_2014 | 2014 | Canada | E078 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3700000 | NA | 0.1890000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1890000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E079 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.5100000 | NA | 0.2320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2320000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E080 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.6850000 | NA | 0.2930000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.2930000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E081 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2210000 | NA | 0.1140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1140000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E082 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.4840000 | NA | 0.2640000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2640000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E083 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1370000 | NA | 0.0510000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0510000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E084 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2480000 | NA | 0.0610000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0610000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E085 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 20.5100000 | NA | 6.7520000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 6.7520000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E086 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.4740000 | NA | 0.1960000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.1960000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E087 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0020000 | NA | 0.0020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0020000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E088 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0180000 | NA | 0.0090000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0090000 | 0.0517671 |
| F003 | Bhavsar_2014 | 2014 | Canada | E089 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3580000 | NA | 0.1700000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1700000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E090 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.5280000 | NA | 0.2330000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2330000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E091 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.7250000 | NA | 0.3450000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.3450000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E092 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2370000 | NA | 0.1110000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1110000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E093 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.5580000 | NA | 0.2800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2800000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E094 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1490000 | NA | 0.0680000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0680000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E095 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2630000 | NA | 0.0870000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0870000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E096 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 22.1100000 | NA | 7.8970000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 7.8970000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E097 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.5600000 | NA | 0.2260000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.2260000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E098 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0120000 | NA | 0.0180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0180000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E099 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0160000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0060000 | 0.0026586 |
| F003 | Bhavsar_2014 | 2014 | Canada | E100 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3740000 | NA | 0.1810000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1810000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E101 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.4930000 | NA | 0.2070000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2070000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E102 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.6830000 | NA | 0.2860000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.2860000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E103 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2320000 | NA | 0.1030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1030000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E104 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.5190000 | NA | 0.2120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2120000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E105 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1290000 | NA | 0.0450000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0450000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E106 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2450000 | NA | 0.0770000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0770000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E107 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 21.6700000 | NA | 8.0080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 8.0080000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E108 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.5160000 | NA | 0.2440000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.2440000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E109 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0020000 | NA | 0.0010000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0010000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E110 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0160000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0060000 | 0.0511604 |
| F003 | Bhavsar_2014 | 2014 | Canada | E111 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0790000 | NA | 0.0230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0230000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E112 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3490000 | NA | 0.0940000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0940000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E113 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3330000 | NA | 0.0910000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0910000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E114 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1330000 | NA | 0.0120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0120000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E115 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.1800000 | NA | 0.0210000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0210000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E116 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0930000 | NA | 0.0230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0230000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E117 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0980000 | NA | 0.0340000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0340000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E118 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 45.0900000 | NA | 3.7090000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 3.7090000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E119 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1780000 | NA | 0.0940000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0940000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E120 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0350000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0060000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E121 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0630000 | NA | 0.0170000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0170000 | 0.0583152 |
| F003 | Bhavsar_2014 | 2014 | Canada | E122 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0740000 | NA | 0.0140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0140000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E123 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3380000 | NA | 0.0980000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0980000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E124 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3480000 | NA | 0.1020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.1020000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E125 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1440000 | NA | 0.0370000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0370000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E126 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.2170000 | NA | 0.0410000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0410000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E127 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0940000 | NA | 0.0250000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0250000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E128 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0880000 | NA | 0.0360000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0360000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E129 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 52.6900000 | NA | 14.6200000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 14.6200000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E130 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1890000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0800000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E131 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0400000 | NA | 0.0080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0080000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E132 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0120000 | 0.0028457 |
| F003 | Bhavsar_2014 | 2014 | Canada | E133 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0670000 | NA | 0.0150000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0150000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E134 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.2990000 | NA | 0.0720000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0720000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E135 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3070000 | NA | 0.0760000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0760000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E136 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1290000 | NA | 0.0490000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0490000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E137 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.1790000 | NA | 0.0540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0540000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E138 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0340000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0340000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E139 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0270000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0270000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E140 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 44.5100000 | NA | 7.7180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 7.7180000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E141 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1570000 | NA | 0.0660000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0660000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E142 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0290000 | NA | 0.0040000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0040000 | 0.0615832 |
| F003 | Bhavsar_2014 | 2014 | Canada | E143 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0770000 | NA | 0.0050000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0050000 | 0.0615832 |
| F005 | DelGobbo_2008 | 2008 | Canada | E144 | Catfish | Ictalurus punctatus | vertebrate | freshwater fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C017 | NA | 19 | 1 | ng/g | 1.5657252 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.8987374 | NA | NA | technical | 4 | ng/g | 0.3646058391 | 1.093817517 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | ML | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E145 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | NA | 14 | 1 | ng/g | 1.3600000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0169896 | LOD | NA | technical | 4 | ng/g | 0.01698962618 | 0.05096887855 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E146 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | NA | 14 | 1 | ng/g | 0.3715856 | LOD | NA | Not available because sample size is one. | technical | 14 | 1 | 0.4700000 | NA | NA | technical | 4 | ng/g | 0.3715856481 | 1.114756944 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E147 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 0.0774969 | LOD | NA | Not available because sample size is one. | technical | 9 | 1 | 0.0600000 | NA | NA | technical | 4 | ng/g | 0.07749693852 | 0.2324908155 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E148 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9 | 1 | 0.0032120 | LOD | NA | technical | 4 | ng/g | 0.0032120281 | 0.009636084301 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E149 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 0.0270203 | LOD | NA | Not available because sample size is one. | technical | 9 | 1 | 0.3900000 | NA | NA | technical | 4 | ng/g | 0.02702032357 | 0.08106097072 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E150 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9 | 1 | 0.2200000 | NA | NA | technical | 4 | ng/g | 0.2333732266 | 0.7001196799 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E151 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 0.7800000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0600000 | NA | NA | technical | 3 | ng/g | 0.02612585327 | 0.0783775598 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E152 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.2900000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0261259 | LOD | NA | technical | 3 | ng/g | 0.02612585327 | 0.0783775598 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E153 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFDA | 10 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0120876 | LOD | NA | technical | 3 | ng/g | 0.01208759187 | 0.03626277562 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E154 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.8800000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 1.5900000 | NA | NA | technical | 3 | ng/g | 0.02340346342 | 0.07021039026 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E155 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 2.6100000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0071943 | LOD | NA | technical | 3 | ng/g | 0.007194278092 | 0.02158283428 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E156 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 0.5086163 | LOD | NA | Not available because sample size is one. | technical | 15 | 1 | 0.2300000 | NA | NA | technical | 3 | ng/g | 0.5086163051 | 1.525848915 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E157 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C021 | NA | 19 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.2100000 | NA | NA | technical | 4 | ng/g | 0.335745729 | 1.007237187 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E158 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C021 | NA | 19 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.7800000 | NA | NA | technical | 4 | ng/g | 0.2127077334 | 0.6381232001 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E159 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | NA | 22 | 1 | ng/g | 1.5800000 | NA | NA | Not available because sample size is one. | technical | 22 | 1 | 1.5900000 | NA | NA | technical | 3 | ng/g | 0.03079926295 | 0.09239778884 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E160 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | NA | 22 | 1 | ng/g | 1.3200000 | NA | NA | Not available because sample size is one. | technical | 22 | 1 | 0.9600000 | NA | NA | technical | 3 | ng/g | 0.004661629686 | 0.01398488906 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E161 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.0900000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0027709 | LOD | NA | technical | 4 | ng/g | 0.002770915071 | 0.008312745212 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E162 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 1.3500000 | NA | NA | technical | 4 | ng/g | 0.01203365344 | 0.03610096033 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E163 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.3300000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0255728 | LOD | NA | technical | 4 | ng/g | 0.02557281543 | 0.07671844628 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E164 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 0.6700000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0070174 | LOD | NA | technical | 4 | ng/g | 0.007017439682 | 0.02105231905 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E165 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.5100000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.8800000 | NA | NA | technical | 4 | ng/g | 0.3642166626 | 1.092649988 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E166 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | NA | 35 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.0179042 | LOD | NA | technical | 4 | ng/g | 0.0179042065 | 0.0537126195 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E167 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | NA | 35 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.8900000 | NA | NA | technical | 4 | ng/g | 0.3768854178 | 1.130656253 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 30.0000000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E168 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | NA | 35 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 2.1100000 | NA | NA | technical | 4 | ng/g | 0.0165860278 | 0.04975808341 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E169 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | NA | 35 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.6800000 | NA | NA | technical | 4 | ng/g | 0.3921755285 | 1.176526586 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA | 0.0625000 |
| F006 | Hu_2020 | 2020 | China | E170 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFBA | 3 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 5.3412073 | NA | 1.6889253 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | ML | 7.4193907 | 1.6889253 | NA |
| F006 | Hu_2020 | 2020 | China | E171 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFOA | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.2674068 | NA | 0.0800584 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0800584 | NA |
| F006 | Hu_2020 | 2020 | China | E172 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFBS | 4 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 23.9801208 | NA | 26.8453690 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 26.8453690 | NA |
| F006 | Hu_2020 | 2020 | China | E173 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFOS | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 122.4133110 | NA | 62.4690572 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 62.4690572 | NA |
| F006 | Hu_2020 | 2020 | China | E174 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFHpA | 7 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 55.3995680 | NA | 55.3995680 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 55.3995680 | NA |
| F006 | Hu_2020 | 2020 | China | E175 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFDoDA | 12 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.2676991 | NA | 1.5334164 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 1.5334164 | NA |
| F006 | Hu_2020 | 2020 | China | E176 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFHxS | 6 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.8685897 | NA | 0.3034431 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.3034431 | NA |
| F006 | Hu_2020 | 2020 | China | E177 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | FOSA | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.3838798 | NA | 1.2904183 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 1.2904183 | NA |
| F006 | Hu_2020 | 2020 | China | E178 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFBA | 3 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 4.9146982 | NA | 7.4344664 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 7.4344664 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E179 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.1932566 | NA | 0.0707998 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0707998 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E180 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFBS | 4 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 10.8230680 | NA | 7.4606797 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 7.4606797 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E181 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFOS | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 97.7348993 | NA | 23.1725546 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 23.1725546 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E182 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFHpA | 7 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 13.7149028 | NA | 23.6036055 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 23.6036055 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E183 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.3534292 | NA | 2.4839931 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 2.4839931 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E184 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFHxS | 6 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.6506410 | NA | 0.1079317 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.1079317 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E185 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | FOSA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.2540984 | NA | 1.2484167 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 1.2484167 | 4.2857143 |
| F006 | Hu_2020 | 2020 | China | E186 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFBA | 3 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 7.9068241 | NA | 9.3812679 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 9.3812679 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E187 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFOA | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.2308114 | NA | 0.1541468 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.1541468 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E188 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFBS | 4 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 9.8657220 | NA | 5.8014926 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 5.8014926 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E189 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFOS | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 134.4379195 | NA | 58.0538019 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 58.0538019 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E190 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFHpA | 7 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 23.7041037 | NA | 35.9297367 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 35.9297367 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E191 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFDoDA | 12 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.8733407 | NA | 2.7470061 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 2.7470061 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E192 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFHxS | 6 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 1.1602564 | NA | 0.7375647 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.7375647 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E193 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | FOSA | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 3.7500000 | NA | 3.7411362 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 3.7411362 | 1.4285714 |
| F006 | Hu_2020 | 2020 | China | E194 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFBA | 3 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 4.8490814 | NA | 6.9303363 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 6.9303363 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E195 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFOA | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.1652961 | NA | 0.0630496 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0630496 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E196 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFBS | 4 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 7.5376305 | NA | 1.5022632 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 1.5022632 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E197 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFOS | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 121.7142058 | NA | 62.5574247 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 62.5574247 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E198 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFHpA | 7 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 10.0971922 | NA | 16.4902451 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 16.4902451 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E199 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFDoDA | 12 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.9120575 | NA | 3.3602781 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 3.3602781 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E200 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFHxS | 6 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.8253205 | NA | 0.2542197 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.2542197 | 0.1428571 |
| F006 | Hu_2020 | 2020 | China | E201 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | FOSA | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.2814208 | NA | 0.4304018 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 0.4304018 | 0.1428571 |
| F007 | Kim_2020 | 2020 | Korea | E202 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. For volume of cooking liquid: 1 cup is 250 ml, accordingly for table spoon etc. | ML | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E203 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1100000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E204 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | NA | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E205 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0600000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E206 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E207 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1300000 | NA | 0.0400000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E208 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1400000 | NA | 0.0100000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E209 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E210 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E211 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E212 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E213 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E214 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E215 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E216 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E217 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E218 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0500000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E219 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0200000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E220 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E221 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E222 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 0.0500000 |
| F007 | Kim_2020 | 2020 | Korea | E223 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E224 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 2.5000000 |
| F007 | Kim_2020 | 2020 | Korea | E225 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA | 7.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E316 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 20.7900000 | NA | 0.1700000 | sd | technical | 5 | 1 | 16.7700000 | NA | 0.4200000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E317 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOS | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.8100000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.7400000 | NA | 0.0300000 | technical | NA | ng/g | 0.07 | 0.07 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E318 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFBA | 3 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.1400000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.0400000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E319 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.3700000 | NA | 0.0300000 | sd | technical | 5 | 1 | 0.3200000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.17 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E320 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 2.8900000 | NA | 0.0200000 | sd | technical | 5 | 1 | 2.3000000 | NA | 0.0300000 | technical | NA | ng/g | 0.03 | 0.08 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E321 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.6600000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.5700000 | NA | 0.0200000 | technical | NA | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E322 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.9300000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.7900000 | NA | 0.0200000 | technical | NA | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E323 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.2500000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.2300000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E324 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTrA | 13 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 1.1200000 | NA | 0.0600000 | sd | technical | 5 | 1 | 1.3800000 | NA | 0.0900000 | technical | NA | ng/g | 0.05 | 0.16 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E325 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTA | 14 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.2800000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.2600000 | NA | 0.0200000 | technical | NA | ng/g | 0.05 | 0.15 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E326 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHxS | 6 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.4800000 | NA | 0.0300000 | sd | technical | 5 | 1 | 0.3300000 | NA | 0.0300000 | technical | NA | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E327 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDS | 10 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.0400000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.0400000 | NA | 0.0100000 | technical | NA | ng/g | 0.09 | 0.27 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F008 | Luo_2019 | 2019 | Korea | E328 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | FOSA | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 1.5400000 | NA | 0.0900000 | sd | technical | 5 | 1 | 2.5500000 | NA | 0.1900000 | technical | NA | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | NA | NA | 2.5000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E329 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1590000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | ML - note shared controls for differend cooking times and methods | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E330 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1170000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E331 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0790000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E332 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E333 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1160000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E334 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E335 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1400000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E336 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1330000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E337 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0710000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E338 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2010000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E339 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0590000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E340 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E341 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 14.7000000 | NA | 0.0090000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E342 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 9.3500000 | NA | 0.0080000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E343 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.6600000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E344 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 5.6300000 | NA | 0.0050000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E345 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 4.5000000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E346 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.7700000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E347 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 8.2800000 | NA | 0.0070000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E348 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 6.6200000 | NA | 0.0060000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E349 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.4800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E350 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 4.4900000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E351 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.0500000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E352 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 2.8300000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E353 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E354 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E355 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E356 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2030000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E357 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1390000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E358 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1040000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E359 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E360 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E361 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E362 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E363 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0510000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E364 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2550000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E365 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 4.7800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E366 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 3.5000000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E367 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.5100000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E368 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 7.0500000 | NA | 0.0060000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E369 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.4700000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E370 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.7600000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E371 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 3.0300000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E372 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.0400000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E373 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.2300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E374 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 4.2800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E375 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.7800000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E376 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.0200000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E377 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E378 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1870000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E379 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E380 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1750000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E381 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1530000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E382 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E383 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E384 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1320000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E385 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0930000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E386 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1810000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E387 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0880000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E388 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0660000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E389 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.1500000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E390 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 2.6500000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E391 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.2300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E392 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.4400000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E393 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 2.3600000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E394 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.6500000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E395 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 3.6800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E396 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.7300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E397 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 0.9200000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E398 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.0300000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E399 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.9700000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E400 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 0.8400000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E401 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E402 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E403 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E404 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E405 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1210000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E406 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E407 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1680000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E408 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1340000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E409 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0910000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E410 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1740000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E411 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E412 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E413 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2760000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E414 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1750000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E415 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E416 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3110000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E417 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E418 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E419 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E420 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1610000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E421 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0850000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E422 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1640000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E423 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0930000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E424 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0670000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E425 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E426 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1460000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E427 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E428 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2120000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E429 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1220000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E430 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E431 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E432 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E433 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0690000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E434 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E435 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E436 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E437 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3720000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E438 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2510000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E439 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E440 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E441 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1800000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E442 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E443 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3260000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E444 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1550000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E445 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0630000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E446 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E447 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E448 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0560000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E449 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E450 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1150000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E451 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0500000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E452 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E453 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E454 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | oil-based | NA | 160 | 1200 | No | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C106 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0570000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E455 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1210000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E456 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0950000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E457 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0430000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E458 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1150000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E459 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E460 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0330000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E461 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6640000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E462 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3120000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E463 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E464 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E465 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3780000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E466 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C106 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E467 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.5980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E468 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.4020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E469 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E470 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E471 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2460000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E472 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E473 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E474 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0620000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E475 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0430000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E476 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0800000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E477 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E478 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E479 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E480 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0700000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E481 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0340000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E482 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0650000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E483 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E484 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0320000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E485 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E486 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1080000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E487 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E488 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E489 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E490 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E491 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1260000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E492 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E493 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0520000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E494 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E495 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0760000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E496 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0490000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E497 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E498 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1130000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E499 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E500 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1520000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E501 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E502 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0610000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E503 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1220000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E504 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E505 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0490000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E506 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E507 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E508 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E509 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3570000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E510 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2100000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E511 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E512 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2560000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E513 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E514 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 0.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E515 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E516 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E517 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E518 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3410000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E519 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E520 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA | 30.0000000 |
| F011 | Taylor_2019 | 2019 | Australia | E521 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFHxS | 6 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 4 | 4 | ng/g | 0.9673000 | NA | 1.0026000 | sd | biological | 4 | 4 | 1.4745000 | NA | 1.7430000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | ML - check empty fields, why SE/SD field is NA? | 1.0026000 | 1.7430000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E522 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 6 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6 | 6 | 84.5499000 | NA | 130.5000000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 133.7000000 | 130.5000000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E523 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.4610420 | 43.3800 | C138 | Clean site | 3 | 3 | ng/g | 0.0894000 | NA | 0.0339000 | sd | biological | 3 | 3 | 0.1210000 | NA | 0.0390000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0339000 | 0.0390000 | 0.4610420 |
| F011 | Taylor_2019 | 2019 | Australia | E526 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFDS | 10 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 2 | 2 | ng/g | 0.1391000 | NA | 0.0247000 | sd | biological | 2 | 2 | 0.3760000 | NA | 0.0240000 | biological | 1 | ng/g | 0.030122517 | 0.10040839 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0247000 | 0.0240000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E527 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | FOSA | 8 | NA | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 2 | 2 | ng/g | 0.0749000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 2 | 2 | 0.1985000 | NA | 0.0120000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0120000 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E528 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFHxS | 6 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C140 | Contaminated site | 5 | 5 | ng/g | 0.7841000 | NA | 0.9602000 | sd | biological | 5 | 5 | 0.8414000 | NA | 1.0420000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.9602000 | 1.0420000 | 0.7696748 |
| F011 | Taylor_2019 | 2019 | Australia | E529 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Contaminated site | 6 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6 | 6 | 70.8427000 | NA | 106.0000000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 133.7000000 | 106.0000000 | 0.7696748 |
| F011 | Taylor_2019 | 2019 | Australia | E530 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.9220839 | 43.3800 | C140 | Clean site | 2 | 2 | ng/g | 0.1090000 | NA | 0.0014000 | sd | biological | 2 | 2 | 0.2005000 | NA | 0.0730000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0014000 | 0.0730000 | 0.9220839 |
| F011 | Taylor_2019 | 2019 | Australia | E533 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | FOSA | 8 | NA | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Contaminated site | 4 | 4 | ng/g | 0.1070000 | NA | 0.0397000 | sd | biological | 4 | 4 | 0.2540000 | NA | 0.1320000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0397000 | 0.1320000 | 0.7696748 |
| F011 | Taylor_2019 | 2019 | Australia | E534 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxA | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 3 | 3 | ng/g | 0.1513000 | NA | 0.0306000 | sd | biological | 3 | 3 | 0.0729200 | NA | 0.0210000 | biological | 1 | ng/g | 0.028099467 | 0.093664888 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0306000 | 0.0210000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E535 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.2070000 | NA | 0.1445000 | sd | biological | 6 | 6 | 0.1086500 | NA | 0.0520000 | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.1445000 | 0.0520000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E536 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.4279000 | NA | 0.2601000 | sd | biological | 6 | 6 | 0.2316000 | NA | 0.1070000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2601000 | 0.1070000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E537 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 4 | 4 | ng/g | 0.0433000 | NA | 0.0137000 | sd | biological | 4 | 4 | 0.0712200 | NA | 0.0660000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0137000 | 0.0660000 | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E538 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 4 | 4 | ng/g | 0.1128000 | NA | 0.0093000 | sd | biological | 4 | 4 | 0.0579700 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0093000 | NA | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E539 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1047000 | NA | NA | sd | biological | 1 | 1 | 0.0579700 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E540 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 1 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1 | 1 | 0.1279700 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E541 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1230000 | NA | NA | sd | biological | 1 | 1 | 0.0802200 | <LOQ | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E542 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.5991000 | NA | 0.2053000 | sd | biological | 6 | 6 | 0.3865700 | NA | 0.0790000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2053000 | 0.0790000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E543 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1230000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1 | 1 | 0.0809900 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E544 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 5.0500000 | NA | 0.4637000 | sd | biological | 6 | 6 | 5.5333300 | NA | 0.8290000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.4637000 | 0.8290000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E545 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 6 | 6 | ng/g | 0.1917000 | NA | 0.2129000 | sd | biological | 6 | 6 | 0.1917100 | NA | 0.2360000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2129000 | 0.2360000 | 40.4858300 |
| F011 | Taylor_2019 | 2019 | Australia | E548 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | FOSA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.3112000 | NA | 0.1413000 | sd | biological | 6 | 6 | 0.3215300 | NA | 0.0990000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.1413000 | 0.0990000 | 45.3309157 |
| F011 | Taylor_2019 | 2019 | Australia | E549 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHpA | 7 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10 | 1 | 0.1279700 | NA | NA | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E550 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 60 | 6 | ng/g | 0.2229000 | NA | 0.0668000 | sd | biological | 60 | 6 | 0.4689700 | NA | 0.1040000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0668000 | 0.1040000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E551 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 60 | 6 | ng/g | 0.0910000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 60 | 6 | 0.2330900 | NA | 0.0370000 | biological | 1 | ng/g | 0.036013573 | 0.120045244 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0370000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E552 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 50 | 5 | ng/g | 0.0854000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 50 | 5 | 0.1877100 | NA | 0.0530000 | biological | 1 | ng/g | 0.039417906 | 0.131393021 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0530000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E553 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHxS | 6 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 60 | 6 | ng/g | 2.3305000 | NA | 1.3905000 | sd | biological | 60 | 6 | 6.3161900 | NA | 1.6280000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 1.3905000 | 1.6280000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E554 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 60 | 6 | ng/g | 7.4167000 | NA | 2.8414000 | sd | biological | 60 | 6 | 16.1667000 | NA | 3.8690000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 2.8414000 | 3.8690000 | 7.7375426 |
| F011 | Taylor_2019 | 2019 | Australia | E555 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 12.5376128 | 39.8800 | C144 | Clean site | 50 | 5 | ng/g | 0.0562000 | NA | 0.0133000 | sd | biological | 50 | 5 | 0.1180000 | NA | 0.0290000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0133000 | 0.0290000 | 12.5376128 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E557 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 23 | 1 | 1.7500000 | NA | 0.0500000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper) | ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E558 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 23 | 1 | 2.9900000 | NA | 0.2200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E559 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 23 | 1 | 6.6200000 | NA | 0.1400000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E560 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 12 | 1 | 0.4400000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3600000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E561 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 12 | 1 | 1.1200000 | NA | 0.0300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3600000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E562 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 12 | 1 | 1.2700000 | NA | 0.0600000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3600000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E563 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 20 | 1 | 0.7000000 | LOD | NA | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E564 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 20 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E565 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 20 | 1 | 0.8300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E566 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 20 | 1 | 1.2400000 | NA | 0.0600000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4300000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E567 | Picarel | Spicara smaris | vertebrate | marine fish | 44.037940 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | NA | 20 | 1 | ng/g | 0.7000000 | NA | 0.0900000 | sd | technical | 20 | 1 | 1.3500000 | NA | 0.0800000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E568 | Picarel | Spicara smaris | vertebrate | marine fish | 44.037940 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | NA | 20 | 1 | ng/g | 20.3700000 | NA | 2.4700000 | sd | technical | 20 | 1 | 44.6900000 | NA | 3.9300000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E569 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 0.3500000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 39 | 1 | 0.7400000 | NA | 0.0900000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5200000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E570 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 1.0800000 | NA | 0.0300000 | sd | technical | 39 | 1 | 1.9800000 | NA | 0.0400000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5200000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E571 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 1.1600000 | NA | 0.0500000 | sd | technical | 39 | 1 | 3.0100000 | NA | 0.1300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5200000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E572 | Sardine | Sardina pilchardus | vertebrate | marine fish | 57.258065 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.8800000 | 340.9091 | C150 | NA | 14 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 14 | 1 | 0.9300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.8800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E573 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 15 | 1 | 0.5700000 | NA | 0.1100000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E574 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 15 | 1 | 0.5600000 | NA | 0.0700000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E575 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 15 | 1 | 0.7300000 | NA | 0.2000000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E576 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 15 | 1 | 1.3800000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E577 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 15 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.5700000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E578 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 4.9400000 | NA | 0.2600000 | sd | technical | 30 | 1 | 14.8800000 | NA | 1.6100000 | technical | 1 | ng/g | 0.39 | 1.17 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E579 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 30 | 1 | 0.9900000 | NA | 0.2100000 | technical | 1 | ng/g | 0.6 | 1.82 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E580 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.2700000 | NA | 0.0700000 | sd | technical | 30 | 1 | 1.5200000 | NA | 0.1100000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E581 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.7300000 | NA | 0.0800000 | sd | technical | 30 | 1 | 1.8100000 | NA | 0.1900000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E582 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 2.7600000 | NA | 0.2100000 | sd | technical | 30 | 1 | 6.8200000 | NA | 0.2200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E583 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.3600000 | NA | 0.0900000 | sd | technical | 30 | 1 | 2.3100000 | NA | 0.0900000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E584 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.3700000 | NA | 0.1600000 | sd | technical | 30 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E585 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 5.1500000 | NA | 0.3900000 | sd | technical | 30 | 1 | 8.0200000 | NA | 0.4200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.4800000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E586 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.867299 | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1950000 | <LOD | NA | sd | technical | 18 | 1 | 5.0600000 | NA | 0.1900000 | technical | 1 | ng/g | 0.39 | 1.17 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E587 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.867299 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 18 | 1 | 0.5100000 | NA | 0.0400000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E588 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.867299 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 18 | 1 | 1.0400000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E589 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.867299 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 18 | 1 | 1.6500000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E590 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.867299 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 18 | 1 | 1.5600000 | NA | 0.1700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.3900000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E591 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 39 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 39 | 1 | 0.8300000 | NA | 0.0100000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E592 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 39 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 39 | 1 | 2.7300000 | NA | 0.1300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E593 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 39 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 39 | 1 | 3.5200000 | NA | 0.1000000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E594 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 39 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 39 | 1 | 6.2900000 | NA | 0.3400000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E595 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 12 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 12 | 1 | 0.4300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E596 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 12 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 12 | 1 | 0.6300000 | NA | 0.0200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E597 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 12 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 12 | 1 | 0.8700000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E598 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 20 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 20 | 1 | 0.8200000 | NA | 0.0300000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E599 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 20 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 20 | 1 | 1.1100000 | NA | 0.1500000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E600 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 20 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 20 | 1 | 1.8900000 | NA | 0.0500000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E601 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFBS | 4 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 20 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 20 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E602 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 20 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 20 | 1 | 2.4000000 | NA | 0.1300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E603 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 14 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 14 | 1 | 0.8700000 | NA | 0.0300000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E604 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 14 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 14 | 1 | 1.7000000 | NA | 0.1300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E605 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 14 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 14 | 1 | 3.1900000 | NA | 0.0900000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E606 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFNA | 9 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 15 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 15 | 1 | 0.5000000 | NA | 0.0500000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E607 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 15 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 15 | 1 | 0.3450000 | <LOD | NA | NA | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E608 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 15 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 15 | 1 | 0.8200000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E609 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 15 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 15 | 1 | 10.2300000 | NA | 0.5300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E610 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFOA | 8 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 18 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 18 | 1 | 0.4000000 | NA | 0.0100000 | technical | 1 | ng/g | 0.6 | 1.82 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E611 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 18 | 1 | 1.0900000 | NA | 0.0200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E612 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 18 | 1 | 1.1900000 | NA | 0.1700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA | 0.0000000 |
Import phylogenetic information and calculate phylogenetic variance-covariance matrix
The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document
tree <- read.tree(here("data", "phylogenetic_tree.tre")) # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details)
tree <- compute.brlen(tree) # Generate branch lengths
cor_tree <- vcv(tree, corr = T) # Generate phylogenetic variance-covariance matrix
dat$Phylogeny <- str_replace(dat$Species_Scientific, " ", "_") # Add the `phylogeny` column to the data frame
colnames(cor_tree) %in% dat$Phylogeny # Check correspondence between tip names and data frame## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEplot(tree)
Calculate effect sizes
The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)
aCV2 <- dat %>%
group_by(Study_ID) %>% # Group by study
summarise(CV2c = mean((SDc/Mc)^2, na.rm = T), # Calculate the squared coefficient of variation for control and experimental groups
CV2e = mean((SDe/Me)^2, na.rm = T)) %>%
ungroup() %>% # ungroup
summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
aCV2e = mean(CV2e, na.rm = T))
effect <- lnRR_func(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]],
rho = 0.5) # Calculate effect sizes
dat <- dat %>%
mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # Calculate the effective sample size
dat <- cbind(dat, effect) # Merge effect sizes with the data frame
VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)Distribution of effect sizes
# mean
ggplot(dat, aes(x = lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) +
theme_classic()
# variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
theme_classic()
# log variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
scale_x_log10() + theme_classic()
Sample sizes
Table of sample sizes
dat %>%
summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
`Studies` = n_distinct(Study_ID),
`Species` = n_distinct(Species_common),
`PFAS type` = n_distinct(PFAS_type),
`Cohorts` = n_distinct(Cohort_ID),
`Effect sizes` = n_distinct(Effect_ID),
`Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
`Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
`Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),
`Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
`Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
`Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),
`Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
`Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
`Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes
table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")| n (sample size) | |
|---|---|
| Studies | 10 |
| Species | 39 |
| PFAS type | 18 |
| Cohorts | 153 |
| Effect sizes | 512 |
| Effect sizes (Oil-based) | 303 |
| Studies (Oil-based) | 7 |
| Species (Oil-based) | 28 |
| Effect sizes (Water-based) | 140 |
| Studies (Water-based) | 8 |
| Species (Water-based) | 23 |
| Effect sizes (No liquid) | 69 |
| Studies (No liquid) | 2 |
| Species (No liquid) | 14 |
Summary of the dataset
kable(summary(dat), "html") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 | Phylogeny | N_tilde | lnRR | var_lnRR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:512 | Length:512 | Min. :2008 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 6.77 | Length:512 | Min. : 3.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 75.0 | Min. : 120.0 | Length:512 | Length:512 | Length:512 | Min. : 0.341 | Min. : 0.0 | Min. : 0.00266 | Min. : 10.0 | Length:512 | Length:512 | Min. : 1.00 | Min. :1.000 | Length:512 | Min. : 0.002 | Length:512 | Min. : 0.0010 | Length:512 | Length:512 | Min. : 1.00 | Min. :1.000 | Min. : 0.0020 | Length:512 | Min. : 0.000 | Length:512 | Min. :1.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 0.0010 | Min. : 0.0010 | Min. : 0.00000 | Length:512 | Min. : 0.500 | Min. :-6.0350 | Min. :0.01399 | |
| Class :character | Class :character | 1st Qu.:2014 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:14.45 | Class :character | 1st Qu.: 8.000 | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:100.0 | 1st Qu.: 600.0 | Class :character | Class :character | Class :character | 1st Qu.: 11.000 | 1st Qu.: 5.0 | 1st Qu.: 0.10004 | 1st Qu.: 10.0 | Class :character | Class :character | 1st Qu.: 5.00 | 1st Qu.:1.000 | Class :character | 1st Qu.: 0.160 | Class :character | 1st Qu.: 0.0010 | Class :character | Class :character | 1st Qu.: 5.00 | 1st Qu.:1.000 | 1st Qu.: 0.0940 | Class :character | 1st Qu.: 0.001 | Class :character | 1st Qu.:1.000 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.: 0.0354 | 1st Qu.: 0.0585 | 1st Qu.: 0.05116 | Class :character | 1st Qu.: 2.500 | 1st Qu.:-0.8778 | 1st Qu.:0.08394 | |
| Mode :character | Mode :character | Median :2019 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :18.35 | Mode :character | Median : 8.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :160.0 | Median : 600.0 | Mode :character | Mode :character | Mode :character | Median : 300.000 | Median : 250.0 | Median : 2.50000 | Median : 70.0 | Mode :character | Mode :character | Median :10.00 | Median :1.000 | Mode :character | Median : 0.298 | Mode :character | Median : 0.0100 | Mode :character | Mode :character | Median :10.00 | Median :1.000 | Median : 0.2285 | Mode :character | Median : 0.020 | Mode :character | Median :3.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median : 0.1580 | Median : 0.1461 | Median : 0.52000 | Mode :character | Median : 5.000 | Median :-0.1671 | Median :0.08394 | |
| NA | NA | Mean :2017 | NA | NA | NA | NA | NA | NA | Mean :21.04 | NA | Mean : 8.994 | NA | NA | NA | NA | NA | Mean :161.3 | Mean : 733.3 | NA | NA | NA | Mean : 271.946 | Mean : 231.8 | Mean :13.58240 | Mean : 149.1 | NA | NA | Mean :10.61 | Mean :2.371 | NA | Mean : 3.494 | NA | Mean : 1.7676 | NA | NA | Mean :10.61 | Mean :2.371 | Mean : 3.2321 | NA | Mean : 1.822 | NA | Mean :2.481 | NA | NA | NA | NA | NA | NA | NA | NA | Mean : 4.4069 | Mean : 4.4491 | Mean :11.68141 | NA | Mean : 5.304 | Mean :-0.3631 | Mean :0.11807 | |
| NA | NA | 3rd Qu.:2019 | NA | NA | NA | NA | NA | NA | 3rd Qu.:21.31 | NA | 3rd Qu.:11.000 | NA | NA | NA | NA | NA | 3rd Qu.:175.0 | 3rd Qu.: 900.0 | NA | NA | NA | 3rd Qu.: 300.000 | 3rd Qu.: 300.0 | 3rd Qu.:30.00000 | 3rd Qu.: 178.4 | NA | NA | 3rd Qu.:10.00 | 3rd Qu.:5.000 | NA | 3rd Qu.: 1.083 | NA | 3rd Qu.: 0.1185 | NA | NA | 3rd Qu.:10.00 | 3rd Qu.:5.000 | 3rd Qu.: 1.0505 | NA | 3rd Qu.: 0.130 | NA | 3rd Qu.:3.000 | NA | NA | NA | NA | NA | NA | NA | NA | 3rd Qu.: 0.5600 | 3rd Qu.: 0.6516 | 3rd Qu.:30.00000 | NA | 3rd Qu.: 5.000 | 3rd Qu.: 0.1849 | 3rd Qu.:0.16787 | |
| NA | NA | Max. :2020 | NA | NA | NA | NA | NA | NA | Max. :79.11 | NA | Max. :14.000 | NA | NA | NA | NA | NA | Max. :300.0 | Max. :1500.0 | NA | NA | NA | Max. :2500.000 | Max. :2500.0 | Max. :45.33092 | Max. :1000.0 | NA | NA | Max. :60.00 | Max. :6.000 | NA | Max. :86.689 | NA | Max. :133.7000 | NA | NA | Max. :60.00 | Max. :6.000 | Max. :134.4379 | NA | Max. :130.500 | NA | Max. :4.000 | NA | NA | NA | NA | NA | NA | NA | NA | Max. :133.7000 | Max. :130.5000 | Max. :45.33092 | NA | Max. :30.000 | Max. : 3.4617 | Max. :0.83936 | |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :284 | NA | NA | NA | NA | NA | NA | NA | NA’s :6 | NA’s :56 | NA | NA | NA | NA’s :114 | NA’s :45 | NA’s :88 | NA’s :106 | NA | NA | NA | NA | NA | NA | NA | NA’s :53 | NA | NA | NA | NA | NA | NA | NA’s :55 | NA | NA’s :198 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :330 | NA’s :328 | NA’s :19 | NA | NA | NA | NA |
Intercept meta-analytical model
Determine the random effect structure
Cohort_ID explains virtually no variance in the model. Hence, it was removed from the model. All the other random effects explained significant variance and were kept in subsequent models
MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
random = list(~1|Study_ID, # Identity of the study
~1|Phylogeny, # Phylogenetic correlation
~1|Cohort_ID, # Identity of the cohort (shared controls)
~1|Species_common, # Non-phylogenetic correlation between species
~1|PFAS_type, # Type of PFAS
~1|Effect_ID), # Effect size identity
R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
test = "t",
data = dat,
sparse = TRUE)
summary(MA_all_rand_effects) # Cohort ID does not explain any variance ##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.2196 1250.4392 1264.4392 1294.0938 1264.6619
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5869 0.7661 10 no Study_ID no
## sigma^2.2 0.0000 0.0004 38 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 153 no Cohort_ID no
## sigma^2.4 0.2185 0.4675 39 no Species_common no
## sigma^2.5 0.0968 0.3112 18 no PFAS_type no
## sigma^2.6 0.4994 0.7067 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 10299.1810, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3238 0.2858 -1.1328 511 0.2578 -0.8853 0.2377
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Intercept meta-analytical model and percentage of heterogeneity
MA_model <- rma.mv(lnRR, VCV_lnRR,
random = list(~1|Study_ID,
~1|Phylogeny, # Removed Cohort_ID
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat,
sparse = TRUE)
summary(MA_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.2196 1250.4392 1262.4392 1287.8574 1262.6059
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5869 0.7661 10 no Study_ID no
## sigma^2.2 0.0000 0.0004 38 no Phylogeny yes
## sigma^2.3 0.2185 0.4675 39 no Species_common no
## sigma^2.4 0.0968 0.3112 18 no PFAS_type no
## sigma^2.5 0.4994 0.7067 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 10299.1810, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3238 0.2858 -1.1328 511 0.2578 -0.8853 0.2377
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1round(i2_ml(MA_model)*100,2) # Percentage of heterogeneity explained by each random effect## I2_total I2_Study_ID I2_Phylogeny I2_Species_common
## 94.65 39.63 0.00 14.76
## I2_PFAS_type I2_Effect_ID
## 6.54 33.72# plot
orchard_plot(MA_model, mod = "Int", xlab = "lnRR", alpha=0.4) + # Orchard plot
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models Single moderator meta-regressions
Function to run all models with the same structure
run_model<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data
, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = data,
sparse=TRUE) # Make the model run faster
}Function to run plots with the same structure
plot_continuous<-function(data, model, moderator, xlab){
pred<-predict.rma(model)
data %>% mutate(fit=pred$pred,
ci.lb=pred$ci.lb,
ci.ub=pred$ci.ub,
pr.lb=pred$cr.lb,
pr.ub=pred$cr.ub) %>% # Add confidence intervals, mean predictions and prediction intervals
ggplot(aes(x = moderator, y = lnRR)) +
geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) + # Shaded area for prediction intervals
geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) + # Shaded area for confidence intervals
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) + # Points scaled by precision
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
geom_line(aes(y = fit), size = 1.5)+ # Regression line
labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
theme_bw() +
scale_size_continuous(range=c(1,9))+ # Point scaling
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
}Single-moderator models
All continuous variables were z-transformed
Cooking time
# Length_cooking_time_in_s
time_model <- run_model(dat, ~scale(Length_cooking_time_in_s)) # z-transformed
summary(time_model)##
## Multivariate Meta-Analysis Model (k = 456; method: REML)
##
## logLik Deviance AIC BIC AICc
## -515.6672 1031.3344 1045.3344 1074.1611 1045.5855
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5327 0.7299 9 no Study_ID no
## sigma^2.2 0.0000 0.0001 30 no Phylogeny yes
## sigma^2.3 0.1758 0.4193 30 no Species_common no
## sigma^2.4 0.0977 0.3125 17 no PFAS_type no
## sigma^2.5 0.4091 0.6396 456 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 454) = 6849.1890, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 454) = 27.7317, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5515 0.2880 -1.9151 454 0.0561
## scale(Length_cooking_time_in_s) -0.2571 0.0488 -5.2661 454 <.0001
## ci.lb ci.ub
## intrcpt -1.1175 0.0144 .
## scale(Length_cooking_time_in_s) -0.3530 -0.1611 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(time_model) # Estimate R squared## R2_marginal R2_coditional
## 0.0515760 0.6807625# Plot
dat.time <- filter(dat, Length_cooking_time_in_s != "NA") # Need to remove the NAs from the data
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")
Ratio between tissue sample and liquid volume, with dry cooking coded as NA
# Ratio_liquid_fish
dat <- dat %>%
mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category == "No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is 'No liquid', otherwise keep the same value of Ratio_liquid_fish
volume_model <- run_model(dat, ~scale(log(Ratio_liquid_fish))) # logged and z-transformed
summary(volume_model)##
## Multivariate Meta-Analysis Model (k = 424; method: REML)
##
## logLik Deviance AIC BIC AICc
## -531.1086 1062.2172 1076.2172 1104.5322 1076.4877
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5545 0.7446 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 34 no Phylogeny yes
## sigma^2.3 0.1528 0.3909 35 no Species_common no
## sigma^2.4 0.1117 0.3342 18 no PFAS_type no
## sigma^2.5 0.5440 0.7376 424 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 422) = 8090.6014, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 422) = 4.1156, p-val = 0.0431
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.4391 0.3005 -1.4613 422 0.1447 -1.0298
## scale(log(Ratio_liquid_fish)) -0.2566 0.1265 -2.0287 422 0.0431 -0.5053
## ci.ub
## intrcpt 0.1516
## scale(log(Ratio_liquid_fish)) -0.0080 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(volume_model)## R2_marginal R2_coditional
## 0.0460951 0.6192765# Plot
dat.volume <- filter(dat, Ratio_liquid_fish != "NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Ratio_liquid_fish), "ln (Liquid volume to tissue sample ratio)")
Ratio between tissue sample and liquid volume, with dry cooking coded as 0
volume0_model <- run_model(dat, ~scale(log(Ratio_liquid_fish_0 + 1))) # logged and z-transformed after adding 1
summary(volume0_model)##
## Multivariate Meta-Analysis Model (k = 493; method: REML)
##
## logLik Deviance AIC BIC AICc
## -596.6179 1193.2357 1207.2357 1236.6108 1207.4676
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6119 0.7822 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 34 no Phylogeny yes
## sigma^2.3 0.2089 0.4570 35 no Species_common no
## sigma^2.4 0.1210 0.3478 18 no PFAS_type no
## sigma^2.5 0.4901 0.7000 493 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 491) = 8975.1652, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 491) = 4.3540, p-val = 0.0374
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.4359 0.3170 -1.3752 491 0.1697
## scale(log(Ratio_liquid_fish_0 + 1)) -0.1158 0.0555 -2.0866 491 0.0374
## ci.lb ci.ub
## intrcpt -1.0587 0.1869
## scale(log(Ratio_liquid_fish_0 + 1)) -0.2248 -0.0068 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(volume0_model)## R2_marginal R2_coditional
## 0.009275669 0.660912937# Plot
dat.volume0 <- filter(dat, Ratio_liquid_fish_0 != "NA")
plot_continuous(dat.volume0, volume0_model, log(dat.volume0$Ratio_liquid_fish_0 +
1), "ln (Liquid volume to tissue sample ratio + 1)")
Cooking temperature
# Temperature_in_Celsius
temp_model <- run_model(dat, ~scale(Temperature_in_Celsius)) # z-transformed
summary(temp_model)##
## Multivariate Meta-Analysis Model (k = 506; method: REML)
##
## logLik Deviance AIC BIC AICc
## -616.6569 1233.3138 1247.3138 1276.8718 1247.5396
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5813 0.7624 10 no Study_ID no
## sigma^2.2 0.0000 0.0003 38 no Phylogeny yes
## sigma^2.3 0.2164 0.4652 39 no Species_common no
## sigma^2.4 0.0934 0.3055 18 no PFAS_type no
## sigma^2.5 0.5009 0.7077 506 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 504) = 10124.5102, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 504) = 0.0257, p-val = 0.8728
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.3086 0.2863 -1.0781 504 0.2815 -0.8711
## scale(Temperature_in_Celsius) 0.0115 0.0720 0.1602 504 0.8728 -0.1300
## ci.ub
## intrcpt 0.2538
## scale(Temperature_in_Celsius) 0.1531
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(temp_model)## R2_marginal R2_coditional
## 9.564442e-05 6.402030e-01# Plot
dat.temp <- filter(dat, Temperature_in_Celsius != "NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")
PFAS carbon chain length
# PFAS_carbon_chain
PFAS_model <- run_model(dat, ~PFAS_carbon_chain)
summary(PFAS_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -623.7325 1247.4651 1261.4651 1291.1059 1261.6882
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5859 0.7654 10 no Study_ID no
## sigma^2.2 0.0000 0.0005 38 no Phylogeny yes
## sigma^2.3 0.2199 0.4689 39 no Species_common no
## sigma^2.4 0.1011 0.3180 18 no PFAS_type no
## sigma^2.5 0.4997 0.7069 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 10271.6160, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1819, p-val = 0.6699
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.4429 0.3991 -1.1097 510 0.2677 -1.2270 0.3412
## PFAS_carbon_chain 0.0131 0.0307 0.4265 510 0.6699 -0.0472 0.0734
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(PFAS_model)## R2_marginal R2_coditional
## 0.0006616066 0.6450021104plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")
Cooking category
# Cooking_Category
category_model<-run_model(dat, ~Cooking_Category-1)
summary(category_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -622.0990 1244.1981 1260.1981 1294.0577 1260.4861
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5906 0.7685 10 no Study_ID no
## sigma^2.2 0.0000 0.0002 38 no Phylogeny yes
## sigma^2.3 0.2204 0.4695 39 no Species_common no
## sigma^2.4 0.0984 0.3137 18 no PFAS_type no
## sigma^2.5 0.4984 0.7060 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 509) = 10270.0672, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 509) = 1.2830, p-val = 0.2795
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Cooking_CategoryNo liquid -0.1996 0.3078 -0.6485 509 0.5170 -0.8044
## Cooking_Categoryoil-based -0.3835 0.2935 -1.3067 509 0.1919 -0.9601
## Cooking_Categorywater-based -0.2938 0.2917 -1.0072 509 0.3143 -0.8668
## ci.ub
## Cooking_CategoryNo liquid 0.4052
## Cooking_Categoryoil-based 0.1931
## Cooking_Categorywater-based 0.2793
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(category_model)## R2_marginal R2_coditional
## 0.003069253 0.647037526# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4)+
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))
Percentage of moisture loss
This analysis is a posteriori and will only be presented in supplement.
# Moisture_loss_in_percent
moisture_model <- run_model(dat, ~scale(Moisture_loss_in_percent))
summary(moisture_model)##
## Multivariate Meta-Analysis Model (k = 228; method: REML)
##
## logLik Deviance AIC BIC AICc
## -223.2698 446.5396 460.5396 484.4834 461.0534
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0830 0.2880 6 no Study_ID no
## sigma^2.2 0.2275 0.4770 18 no Phylogeny yes
## sigma^2.3 0.1212 0.3482 18 no Species_common no
## sigma^2.4 0.0089 0.0943 17 no PFAS_type no
## sigma^2.5 0.3065 0.5536 228 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 226) = 2666.9842, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 226) = 0.0970, p-val = 0.7557
##
## Model Results:
##
## estimate se tval df pval
## intrcpt 0.5244 0.3278 1.6000 226 0.1110
## scale(Moisture_loss_in_percent) -0.0206 0.0662 -0.3115 226 0.7557
## ci.lb ci.ub
## intrcpt -0.1214 1.1703
## scale(Moisture_loss_in_percent) -0.1512 0.1099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(moisture_model)## R2_marginal R2_coditional
## 0.0005696012 0.5900096113# Plot
dat.moisture <- filter(dat, Moisture_loss_in_percent != "NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent,
"Percentage of moisture loss")
save(category_model, PFAS_model, temp_model, time_model, volume_model, volume0_model,
moisture_model, file = here("Rdata", "single_mod_models.RData")) # Save modelsFull models
Full model with Ratio_liquid_fish as NA for the dry cooking category
# Testing cooking categories
full_model <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)),
random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
~1 | Effect_ID), R = list(Phylogeny = cor_tree), test = "t", data = dat,
sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -429.8485 859.6970 881.6970 924.9808 882.4183
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.3923 0.6264 7 no Study_ID no
## sigma^2.2 0.0000 0.0001 26 no Phylogeny yes
## sigma^2.3 0.1527 0.3908 26 no Species_common no
## sigma^2.4 0.1222 0.3495 17 no PFAS_type no
## sigma^2.5 0.4114 0.6414 384 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 378) = 5583.1852, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 378) = 9.7331, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.8549 0.3065 -2.7891 378 0.0056
## Cooking_Categoryoil-based 0.1328 0.1638 0.8103 378 0.4183
## scale(Temperature_in_Celsius) -0.3457 0.1270 -2.7214 378 0.0068
## scale(Length_cooking_time_in_s) -0.3329 0.0562 -5.9224 378 <.0001
## scale(PFAS_carbon_chain) 0.0625 0.0798 0.7835 378 0.4338
## scale(log(Ratio_liquid_fish)) -0.8208 0.1703 -4.8201 378 <.0001
## ci.lb ci.ub
## intrcpt -1.4576 -0.2522 **
## Cooking_Categoryoil-based -0.1894 0.4549
## scale(Temperature_in_Celsius) -0.5955 -0.0959 **
## scale(Length_cooking_time_in_s) -0.4434 -0.2224 ***
## scale(PFAS_carbon_chain) -0.0944 0.2194
## scale(log(Ratio_liquid_fish)) -1.1556 -0.4860 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model)## R2_marginal R2_coditional
## 0.3624154 0.7567945save(full_model, file = here("Rdata", "full_model.RData"))Full model with Ratio_liquid_fish as 0 for the dry cooking category
full_model0 <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 +
1)), random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
~1 | Effect_ID), R = list(Phylogeny = cor_tree), test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model0)##
## Multivariate Meta-Analysis Model (k = 431; method: REML)
##
## logLik Deviance AIC BIC AICc
## -454.8736 909.7471 933.7471 982.3439 934.5063
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4448 0.6670 7 no Study_ID no
## sigma^2.2 0.3920 0.6261 26 no Phylogeny yes
## sigma^2.3 0.0428 0.2069 26 no Species_common no
## sigma^2.4 0.1350 0.3675 17 no PFAS_type no
## sigma^2.5 0.3568 0.5974 431 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 424) = 5645.3619, p-val < .0001
##
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 424) = 12.2941, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -2.3705 0.5383 -4.4036 424 <.0001
## Cooking_Categoryoil-based 1.6785 0.3492 4.8060 424 <.0001
## Cooking_Categorywater-based 1.9619 0.3904 5.0261 424 <.0001
## scale(Temperature_in_Celsius) 0.0125 0.0977 0.1284 424 0.8979
## scale(Length_cooking_time_in_s) -0.3724 0.0497 -7.4879 424 <.0001
## scale(PFAS_carbon_chain) 0.0663 0.0808 0.8203 424 0.4125
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8690 0.1546 -5.6228 424 <.0001
## ci.lb ci.ub
## intrcpt -3.4286 -1.3124 ***
## Cooking_Categoryoil-based 0.9920 2.3650 ***
## Cooking_Categorywater-based 1.1947 2.7292 ***
## scale(Temperature_in_Celsius) -0.1794 0.2045
## scale(Length_cooking_time_in_s) -0.4702 -0.2747 ***
## scale(PFAS_carbon_chain) -0.0925 0.2251
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1728 -0.5652 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model0)## R2_marginal R2_coditional
## 0.4272399 0.8509826save(full_model0, file = here("Rdata", "full_model.RData"))Check collinearity of predictors
## Check for collinerarity - seems fine
vif(full_model)##
## Cooking_Categoryoil-based scale(Temperature_in_Celsius)
## 2.1672 3.2152
## scale(Length_cooking_time_in_s) scale(PFAS_carbon_chain)
## 1.0589 1.0001
## scale(log(Ratio_liquid_fish))
## 1.7857vif(full_model0)##
## Cooking_Categoryoil-based Cooking_Categorywater-based
## 14.4504 14.2450
## scale(Temperature_in_Celsius) scale(Length_cooking_time_in_s)
## 2.1216 1.0727
## scale(PFAS_carbon_chain) scale(log(Ratio_liquid_fish_0 + 1))
## 1.0000 9.5105dat %>%
select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, Ratio_liquid_fish) %>%
ggpairs() # Estimate correlations between the variables
Conditional analyses
Inspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.
Note that these analyses were not performed separately using full models with Ratio_liquid_fish taken as NA or 0. Indeed, a full model containing the dry cooking category and the liquid ratio would extrapolate predictions for the dry cooking category at the mean liquid ratio; which is incorrect. Therefore, all full models were ran with the data containing NA for the Ratio_liquid_fish of the dry cooking method; and separate models were ran with a data subset only containing the dry cooking method.
Full model
# Full model in original units (no z-transformation)
dat$log_Ratio_liquid_fish <- log(dat$Ratio_liquid_fish)
full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), but without the 'No
# liquid' data This model will be used for conditional analyses on the volume
# of liquid, where the data without liquid is irrelevant
dat_oil_water <- filter(dat, Cooking_Category != "No liquid")
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), with Ratio_liquid_fish_0
dat$log_Ratio_liquid_fish0 <- log(dat$Ratio_liquid_fish_0 + 1)
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# Data subset only containing the data with the dry cooking method. Here, only
# the cooking time was added because the liquid ratio, cooking temperature, and
# PFAS carbon chain length do not have sufficient variability.
dat_dry <- filter(dat, Cooking_Category == "No liquid")
full_model_org_units_dry <- run_model(dat_dry, ~Length_cooking_time_in_s)
save(full_model_org_units, full_model_org_units_dry, full_model_org_units0, full_model_org_units_oil_water,
file = here("Rdata", "full_models_org_units.RData"))Overall marginalised mean
Overall mean with Ratio_liquid_fish taken as NA for the dry cooking category
res <- marginal_means(model = full_model_org_units, data = dat, mod = "1")
res$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.8149356 -1.380655 -0.2492158 -2.93396 1.304089Overall mean with Ratio_liquid_fish taken as 0 for the dry cooking category
res0 <- marginal_means(model = full_model_org_units0, data = dat, mod = "1")
res0$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -1.263789 -2.142098 -0.3854808 -3.727568 1.199989Marginal means for different cooking categories
Overall mean for the water- and oil-based cooking
res_cat <- marginal_means(full_model_org_units, data = dat, mod = "1", by = "Cooking_Category")
res_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt oil-based -0.7485542 -1.318746 -0.1783625 -2.868777 1.371669
## 2 Intrcpt water-based -0.8813171 -1.486997 -0.2756374 -3.011358 1.248724orchard_plot(res_cat, xlab = "lnRR", condition.lab = "Cooking Category")
Overall mean for the dry cooking
res_dry <- marginal_means(full_model_org_units_dry, data = dat, mod = "1")
res_dry$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.5699711 -1.000248 -0.1396944 -1.349651 0.2097082orchard_plot(res_dry, xlab = "lnRR", condition.lab = "Cooking Category")
Marginal means for pre-determined cooking times
Here, we generate estimates at cooking times of 2, 10, and 25 min.
Marginal means for pre-determined cooking times, with Ratio_liquid_fish taken as NA for the dry cooking category
res_cooking_time <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,
600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.1314467 -0.7226344 0.4597409 -2.257412 1.9945190
## 2 Intrcpt 600 -0.6782379 -1.2418802 -0.1145955 -2.796709 1.4402333
## 3 Intrcpt 1500 -1.7034712 -2.3635489 -1.0433934 -3.849614 0.4426719orchard_plot(res_cooking_time, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginal means for pre-determined cooking times, with Ratio_liquid_fish taken as 0 for the dry cooking category
res_cooking_time0 <- marginal_means(full_model_org_units0, data = dat, mod = "1",
at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.475072 -1.354299 0.4041545 -2.939178 1.9890341
## 2 Intrcpt 600 -1.086745 -1.961002 -0.2124874 -3.549082 1.3755925
## 3 Intrcpt 1500 -2.233632 -3.175419 -1.2918439 -4.720747 0.2534839orchard_plot(res_cooking_time0, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginalised means for the water- and oil-based cooking categories
res_cooking_time_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category",
at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 120 -0.06506529 -0.6659512 0.53582064 -2.193748 2.0636177
## 2 Water-based 120 -0.19782819 -0.8221921 0.42653573 -2.333257 1.9376010
## 3 Oil-based 600 -0.61185640 -1.1811266 -0.04258619 -2.731832 1.5081191
## 4 Water-based 600 -0.74461931 -1.3472845 -0.14195414 -2.873805 1.3845666
## 5 Oil-based 1500 -1.63708974 -2.2946278 -0.97955163 -3.782453 0.5082736
## 6 Water-based 1500 -1.76985264 -2.4705219 -1.06918337 -3.928826 0.3891205orchard_plot(res_cooking_time_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginalised means for the dry cooking category
res_cooking_time_dry <- marginal_means(full_model_org_units_dry, data = dat, at = list(Length_cooking_time_in_s = c(120,
600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_dry$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 0.3317270 -0.2248785 0.88833238 -0.5241769 1.1876308
## 2 Intrcpt 600 -0.3552811 -0.8018081 0.09124584 -1.1440448 0.4334826
## 3 Intrcpt 1500 -1.6434213 -2.1519525 -1.13489008 -2.4688701 -0.8179724orchard_plot(res_cooking_time_dry, xlab = "lnRR", condition.lab = "Cooking time (sec)")
Marginal means for different volumes of liquid
Ratio_liquid_fish taken as NA for the dry cooking category
Here, we generate marginalised estimates at volumes of liquid of ~0.1mL/g of tissue, ~10 ml/g of tissue, or 45 mL/g of tissue. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used. We also only used the data on oil and water because the “No liquid” category is not relevant for this analysis when considering Ratio_liquid_fish as NA.
res_volume <- marginal_means(full_model_org_units_oil_water, data = dat_oil_water,
mod = "1", at = list(log_Ratio_liquid_fish = c(log(0.1), log(10), log(45))),
by = "log_Ratio_liquid_fish")
res_volume$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -2.302585 -0.1123975 -0.729584 0.5047890 -2.245739 2.0209441
## 2 Intrcpt 2.302585 -1.2944658 -1.905046 -0.6838858 -3.425905 0.8369739
## 3 Intrcpt 3.806662 -1.6805366 -2.366759 -0.9943140 -3.834865 0.4737913orchard_plot(res_volume, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio) (mL/g)")
Ratio_liquid_fish taken as 0 for the dry cooking category
Here, we generate marginalised estimates at volumes of liquid of 0mL/g of tissue (dry cooking), ~10 ml/g of tissue, or 45 mL/g of tissue.
res_volume0 <- marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(log_Ratio_liquid_fish0 = c(0,
log(10 + 1), log(45 + 1))), by = "log_Ratio_liquid_fish0")
res_volume0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 0.000000 -0.353901 -1.229450 0.5216482 -2.816697 2.10889536
## 2 Intrcpt 2.397895 -1.681350 -2.598619 -0.7640817 -4.159285 0.79658452
## 3 Intrcpt 3.828641 -2.473396 -3.517978 -1.4288139 -5.001228 0.05443521orchard_plot(res_volume0, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio) (mL/g)")
Marginal means for different PFAS carbon chains
Overall mean at the mean of each other predictor, with Ratio_liquid_fish taken as NA for the dry cooking category
Here, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains
res_PFAS <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3,
6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -0.9775760 -1.677363 -0.2777887 -3.136263 1.181111
## 2 Intrcpt 6 -0.8970942 -1.500500 -0.2936879 -3.026490 1.232302
## 3 Intrcpt 12 -0.7361305 -1.334185 -0.1380757 -2.864016 1.391755orchard_plot(res_PFAS, xlab = "lnRR", condition.lab = "PFAS carbon chain")
Overall mean at the mean of each other predictor, with Ratio_liquid_fish taken as 0 for the dry cooking category
res_PFAS0 <- marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3,
6, 12)), by = "PFAS_carbon_chain")
res_PFAS0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -1.432941 -2.406561 -0.4593210 -3.932284 1.066402
## 2 Intrcpt 6 -1.347606 -2.251942 -0.4432701 -3.820783 1.125571
## 3 Intrcpt 12 -1.176937 -2.076077 -0.2777968 -3.648218 1.294345orchard_plot(res_PFAS0, xlab = "lnRR", condition.lab = "PFAS carbon chain")
Marginalised mean estimate for the water- and oil-based cooking categories
res_PFAS_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category",
at = list(PFAS_carbon_chain = c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 3 -0.9111946 -1.614199 -0.20819061 -3.070926 1.248537
## 2 Water-based 3 -1.0439575 -1.776814 -0.31110105 -3.213590 1.125675
## 3 Oil-based 6 -0.8307127 -1.438078 -0.22334783 -2.961234 1.299808
## 4 Water-based 6 -0.9634756 -1.604720 -0.32223105 -3.103901 1.176950
## 5 Oil-based 12 -0.6697490 -1.272264 -0.06723429 -2.798892 1.459394
## 6 Water-based 12 -0.8025120 -1.438282 -0.16674190 -2.941304 1.336280orchard_plot(res_PFAS_cat, xlab = "lnRR", condition.lab = "PFAS carbon chain")
Sub-group analyses for each cooking category
Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.
Oil-based cooking
Filter data
oil_dat <- filter(dat, Cooking_Category == "oil-based")Full model
full_model_oil <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
summary(full_model_oil)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -176.0279 352.0558 372.0558 407.5854 372.9465
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1147 0.3387 6 no Study_ID no
## sigma^2.2 0.0000 0.0000 19 no Phylogeny yes
## sigma^2.3 0.0252 0.1586 19 no Species_common no
## sigma^2.4 0.0485 0.2203 16 no PFAS_type no
## sigma^2.5 0.1293 0.3596 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 1004.4883, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 17.9272, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5750 0.1811 -3.1746 258 0.0017
## scale(Temperature_in_Celsius) -0.0868 0.1173 -0.7404 258 0.4598
## scale(Length_cooking_time_in_s) -0.3782 0.0468 -8.0906 258 <.0001
## scale(PFAS_carbon_chain) 0.1283 0.0613 2.0923 258 0.0374
## scale(log(Ratio_liquid_fish)) -0.2048 0.2022 -1.0129 258 0.3121
## ci.lb ci.ub
## intrcpt -0.9317 -0.2183 **
## scale(Temperature_in_Celsius) -0.3178 0.1441
## scale(Length_cooking_time_in_s) -0.4703 -0.2862 ***
## scale(PFAS_carbon_chain) 0.0076 0.2491 *
## scale(log(Ratio_liquid_fish)) -0.6030 0.1934
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_oil)## R2_marginal R2_coditional
## 0.4421662 0.7730077save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))Full model, with Ratio_liquid_fish taken as 0 for the dry cooking category
full_model_oil0 <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_oil0)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -174.9078 349.8156 369.8156 405.3452 370.7062
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1110 0.3332 6 no Study_ID no
## sigma^2.2 0.0000 0.0000 19 no Phylogeny yes
## sigma^2.3 0.0225 0.1501 19 no Species_common no
## sigma^2.4 0.0509 0.2257 16 no PFAS_type no
## sigma^2.5 0.1287 0.3587 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 1001.1583, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 18.4863, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5811 0.1787 -3.2522 258 0.0013
## scale(Temperature_in_Celsius) -0.0158 0.0786 -0.2012 258 0.8407
## scale(Length_cooking_time_in_s) -0.3791 0.0465 -8.1480 258 <.0001
## scale(PFAS_carbon_chain) 0.1287 0.0621 2.0738 258 0.0391
## scale(log(Ratio_liquid_fish_0 + 1)) -0.3162 0.1809 -1.7479 258 0.0817
## ci.lb ci.ub
## intrcpt -0.9330 -0.2293 **
## scale(Temperature_in_Celsius) -0.1706 0.1390
## scale(Length_cooking_time_in_s) -0.4708 -0.2875 ***
## scale(PFAS_carbon_chain) 0.0065 0.2509 *
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6724 0.0400 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_oil0)## R2_marginal R2_coditional
## 0.5608473 0.8195491save(full_model_oil0, file = here("Rdata", "full_model_oil0.RData"))Water-based cooking
Filter data
water_dat <- filter(dat, Cooking_Category == "water-based")Full model
full_model_water <- run_model(water_dat, ~scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.5238 357.0476 375.0476 399.9072 376.7299
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6093 0.7806 6 no Study_ID no
## sigma^2.2 0.0000 0.0001 19 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 19 no Species_common no
## sigma^2.4 0.5375 0.7331 15 no PFAS_type no
## sigma^2.5 0.9355 0.9672 121 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 117) = 4284.5746, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.4274, p-val = 0.0055
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3292 0.4193 -3.1701 117 0.0019
## scale(Length_cooking_time_in_s) -0.3766 0.1582 -2.3809 117 0.0189
## scale(PFAS_carbon_chain) -0.0478 0.1807 -0.2643 117 0.7920
## scale(log(Ratio_liquid_fish)) -0.6671 0.2502 -2.6665 117 0.0088
## ci.lb ci.ub
## intrcpt -2.1595 -0.4988 **
## scale(Length_cooking_time_in_s) -0.6898 -0.0633 *
## scale(PFAS_carbon_chain) -0.4057 0.3102
## scale(log(Ratio_liquid_fish)) -1.1626 -0.1716 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_water)## R2_marginal R2_coditional
## 0.2189730 0.6491153Full model, with Ratio_liquid_fish taken as 0 for the dry cooking category
full_model_water0 <- run_model(water_dat, ~scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_water0)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.5105 357.0211 375.0211 399.8806 376.7033
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5984 0.7736 6 no Study_ID no
## sigma^2.2 0.0000 0.0002 19 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 19 no Species_common no
## sigma^2.4 0.5391 0.7342 15 no PFAS_type no
## sigma^2.5 0.9350 0.9670 121 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 117) = 4273.0145, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.4670, p-val = 0.0052
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3227 0.4163 -3.1775 117 0.0019
## scale(Length_cooking_time_in_s) -0.3714 0.1579 -2.3514 117 0.0204
## scale(PFAS_carbon_chain) -0.0483 0.1809 -0.2668 117 0.7901
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6528 0.2428 -2.6890 117 0.0082
## ci.lb ci.ub
## intrcpt -2.1472 -0.4983 **
## scale(Length_cooking_time_in_s) -0.6842 -0.0586 *
## scale(PFAS_carbon_chain) -0.4065 0.3100
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1336 -0.1720 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_water0)## R2_marginal R2_coditional
## 0.2146124 0.6456631Comparison between steaming and other water-based cooking categories
In our data set, the studies using steaming-based cooking were considered to have an unknown (i.e. NA) because of the difficulty to assess how much liquid gets in contact with the products. Here, we provide an analysis to compare steaming with other water-based cooking categories
water_dat$steamed<-ifelse(water_dat$Cooking_method=="Steaming","steamed","other") # create a dummy variable to differentiate "steaming" with other types of water-based cooking
full_model_water_steamed <- run_model(water_dat, ~ -1 + # without intercept
steamed +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain)) # In this case, we need to remove the Ratio liquid fish from the model. Otherwise, it would remove observations where the liquid volume was unknown.
summary(full_model_water_steamed)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.5198 421.0395 439.0395 465.2534 440.4681
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6974 0.8351 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 23 no Phylogeny yes
## sigma^2.3 0.0710 0.2665 23 no Species_common no
## sigma^2.4 0.2633 0.5132 15 no PFAS_type no
## sigma^2.5 0.9749 0.9874 140 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 136) = 4955.9622, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 136) = 1.8618, p-val = 0.1207
##
## Model Results:
##
## estimate se tval df pval
## steamedother -0.7137 0.3876 -1.8413 136 0.0678
## steamedsteamed -0.5559 0.4508 -1.2333 136 0.2196
## scale(Length_cooking_time_in_s) -0.3094 0.1591 -1.9446 136 0.0539
## scale(PFAS_carbon_chain) -0.0504 0.1406 -0.3585 136 0.7205
## ci.lb ci.ub
## steamedother -1.4803 0.0528 .
## steamedsteamed -1.4474 0.3355
## scale(Length_cooking_time_in_s) -0.6240 0.0052 .
## scale(PFAS_carbon_chain) -0.3285 0.2277
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Contrast between steamed and non-steamed
full_model_water_steamed_cont <- run_model(water_dat,
~ steamed + # with intercept
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain))
summary(full_model_water_steamed_cont)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.5198 421.0395 439.0395 465.2534 440.4681
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6974 0.8351 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 23 no Phylogeny yes
## sigma^2.3 0.0710 0.2665 23 no Species_common no
## sigma^2.4 0.2633 0.5132 15 no PFAS_type no
## sigma^2.5 0.9749 0.9874 140 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 136) = 4955.9622, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 136) = 1.3648, p-val = 0.2563
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.7137 0.3876 -1.8413 136 0.0678
## steamedsteamed 0.1578 0.4032 0.3913 136 0.6962
## scale(Length_cooking_time_in_s) -0.3094 0.1591 -1.9446 136 0.0539
## scale(PFAS_carbon_chain) -0.0504 0.1406 -0.3585 136 0.7205
## ci.lb ci.ub
## intrcpt -1.4803 0.0528 .
## steamedsteamed -0.6396 0.9552
## scale(Length_cooking_time_in_s) -0.6240 0.0052 .
## scale(PFAS_carbon_chain) -0.3285 0.2277
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1save(full_model_water, full_model_water_steamed, full_model_water_steamed_cont, file = here("Rdata", "full_model_water.RData"))Dry cooking
Filter data
dry_dat <- filter(dat, Cooking_Category == "No liquid")Full model
full_model_dry <- run_model(dry_dat, ~scale(Length_cooking_time_in_s))
summary(full_model_dry)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.3971 22.7942 34.7942 45.6342 37.0047
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0000 0.0000 1 yes Study_ID no
## sigma^2.2 0.0082 0.0908 8 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 8 no Species_common no
## sigma^2.4 0.0727 0.2696 2 no PFAS_type no
## sigma^2.5 0.0233 0.1525 47 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 99.2612, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 38.7631, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.7755 0.2110 -3.6762 45 0.0006 -1.2004
## scale(Length_cooking_time_in_s) -0.3524 0.0566 -6.2260 45 <.0001 -0.4664
## ci.ub
## intrcpt -0.3506 ***
## scale(Length_cooking_time_in_s) -0.2384 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(full_model_dry)## R2_marginal R2_coditional
## 0.5436705 0.8981562save(full_model_dry, file = here("Rdata", "full_model_dry.RData"))Plots
Cooking time
Generate predictions
oil_dat <- filter(dat, Cooking_Category=="oil-based")
water_dat <- filter(dat, Cooking_Category=="water-based")
dry_dat <- filter(dat, Cooking_Category=="No liquid")
oil_dat_time<-filter(oil_dat, Length_cooking_time_in_s!="NA")
water_dat_time<-filter(water_dat, Length_cooking_time_in_s!="NA")
dry_dat_time<-filter(dry_dat, Length_cooking_time_in_s!="NA")
model_oil_time<-run_model(oil_dat_time, ~Length_cooking_time_in_s)
model_water_time<-run_model(water_dat_time, ~Length_cooking_time_in_s)
model_dry_time<-run_model(dry_dat_time, ~Length_cooking_time_in_s)
pred_oil_time<-predict.rma(model_oil_time)
pred_water_time<-predict.rma(model_water_time)
pred_dry_time<-predict.rma(model_dry_time)
oil_dat_time<-mutate(oil_dat_time,
ci.lb = pred_oil_time$ci.lb, # lower bound of the confidence interval for oil
ci.ub = pred_oil_time$ci.ub, # upper bound of the confidence interval for oil
fit = pred_oil_time$pred) # regression line for oil
water_dat_time<-mutate(water_dat_time,
ci.lb = pred_water_time$ci.lb, # lower bound of the confidence interval for water
ci.ub = pred_water_time$ci.ub, # upper bound of the confidence interval for water
fit = pred_water_time$pred) # regression line for water
dry_dat_time<-mutate(dry_dat_time,
ci.lb = pred_dry_time$ci.lb, # lower bound of the confidence interval for dry
ci.ub = pred_dry_time$ci.ub, # upper bound of the confidence interval for dry
fit = pred_dry_time$pred) # regression line for dryPlot
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_time,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_time,aes(y = fit), size = 1.5, col="goldenrod")+
geom_ribbon(data=dry_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=dry_dat_time,aes(y = fit), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category
##### Oil based
full_model_oil_time<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Predictions with the full model, with Ratio_liquid_fish taken as 0 for the dry cooking category
##### Oil based
full_model_oil_time0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Liquid volume to tissue sample ratio
Single moderator models
oil_dat_vol <- filter(oil_dat, Ratio_liquid_fish != "NA")
water_dat_vol <- filter(water_dat, Ratio_liquid_fish != "NA")
model_oil_vol <- run_model(oil_dat_vol, ~log(Ratio_liquid_fish))
model_water_vol <- run_model(water_dat_vol, ~log(Ratio_liquid_fish))
pred_oil_vol <- predict.rma(model_oil_vol)
pred_water_vol <- predict.rma(model_water_vol)
oil_dat_vol <- mutate(oil_dat_vol, ci.lb = pred_oil_vol$ci.lb, ci.ub = pred_oil_vol$ci.ub,
fit = pred_oil_vol$pred)
water_dat_vol <- mutate(water_dat_vol, ci.lb = pred_water_vol$ci.lb, ci.ub = pred_water_vol$ci.ub,
fit = pred_water_vol$pred)
oil_dat$log_Ratio_liquid_fish <- log(oil_dat$Ratio_liquid_fish)
water_dat$log_Ratio_liquid_fish <- log(water_dat$Ratio_liquid_fish)Plot
ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_vol, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = oil_dat_vol, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue ratio (mL/g))",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))
Predictions with the full model
##### Oil based
full_model_oil_vol <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio (mL/g))",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full model
PFAS carbon chain length
Generate predictions with single moderator models
oil_dat_PFAS <- filter(oil_dat, PFAS_carbon_chain != "NA")
water_dat_PFAS <- filter(water_dat, PFAS_carbon_chain != "NA")
dry_dat_PFAS <- filter(dry_dat, PFAS_carbon_chain != "NA")
model_oil_PFAS <- run_model(oil_dat_PFAS, ~PFAS_carbon_chain)
model_water_PFAS <- run_model(water_dat_PFAS, ~PFAS_carbon_chain)
model_dry_PFAS <- run_model(dry_dat_PFAS, ~PFAS_carbon_chain)
pred_oil_PFAS <- predict.rma(model_oil_PFAS)
pred_water_PFAS <- predict.rma(model_water_PFAS)
pred_dry_PFAS <- predict.rma(model_dry_PFAS)
oil_dat_PFAS <- mutate(oil_dat_PFAS, ci.lb = pred_oil_PFAS$ci.lb, ci.ub = pred_oil_PFAS$ci.ub,
fit = pred_oil_PFAS$pred)
water_dat_PFAS <- mutate(water_dat_PFAS, ci.lb = pred_water_PFAS$ci.lb, ci.ub = pred_water_PFAS$ci.ub,
fit = pred_water_PFAS$pred)
dry_dat_PFAS <- mutate(dry_dat_PFAS, ci.lb = pred_dry_PFAS$ci.lb, ci.ub = pred_dry_PFAS$ci.ub,
fit = pred_dry_PFAS$pred)Plot
For some reason the plot doesn’t want to knit, although the script works
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + ggplot(dat,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + aes(x
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + PFAS_carbon_chain,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + y
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + lnRR,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + fill
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + Cooking_Category))
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + +
geom_ribbon(data = dry_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data = dry_dat_PFAS, aes(y = fit), size = 1.5, col = "palegreen3") +
geom_ribbon(data = water_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_PFAS, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data = oil_dat_PFAS, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category
##### Oil based
full_model_oil_PFAS<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category
##### Oil based
full_model_oil_PFAS0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Publication bias
Funnel plot
Funnel plot with Ratio_liquid_fish taken as NA for the dry cooking category
funnel(full_model, yaxis = "seinv")
funnel(full_model)
Funnel plot with Ratio_liquid_fish taken as 0 for the dry cooking category
funnel(full_model0, yaxis = "seinv")
funnel(full_model0)
Egger regressions
Full model with Ratio_liquid_fish taken as NA for the dry cooking category
egger_all <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(egger_all)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -423.1846 846.3691 872.3691 923.4538 873.3747
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0322 0.1795 7 no Study_ID no
## sigma^2.2 0.0000 0.0001 26 no Phylogeny yes
## sigma^2.3 0.1713 0.4139 26 no Species_common no
## sigma^2.4 0.1229 0.3506 17 no PFAS_type no
## sigma^2.5 0.4094 0.6399 384 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 376) = 4946.9908, p-val < .0001
##
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 376) = 13.1570, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.5437 0.3480 -1.5623 376 0.1191
## Cooking_Categorywater-based -0.6973 0.3472 -2.0087 376 0.0453
## I(sqrt(1/N_tilde)) -0.1395 0.5576 -0.2502 376 0.8026
## scale(Publication_year) 0.4358 0.0931 4.6814 376 <.0001
## scale(Temperature_in_Celsius) -0.3602 0.1235 -2.9166 376 0.0038
## scale(Length_cooking_time_in_s) -0.3360 0.0535 -6.2849 376 <.0001
## scale(PFAS_carbon_chain) 0.0817 0.0797 1.0244 376 0.3063
## scale(log(Ratio_liquid_fish)) -0.9279 0.1510 -6.1434 376 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.2279 0.1406
## Cooking_Categorywater-based -1.3799 -0.0147 *
## I(sqrt(1/N_tilde)) -1.2360 0.9569
## scale(Publication_year) 0.2527 0.6188 ***
## scale(Temperature_in_Celsius) -0.6030 -0.1174 **
## scale(Length_cooking_time_in_s) -0.4411 -0.2309 ***
## scale(PFAS_carbon_chain) -0.0751 0.2385
## scale(log(Ratio_liquid_fish)) -1.2249 -0.6309 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1egger_n <- run_model(dat, ~I(sqrt(1/N_tilde)))
summary(egger_n)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -623.4560 1246.9121 1260.9121 1290.5530 1261.1352
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5994 0.7742 10 no Study_ID no
## sigma^2.2 0.0000 0.0003 38 no Phylogeny yes
## sigma^2.3 0.2145 0.4631 39 no Species_common no
## sigma^2.4 0.0970 0.3115 18 no PFAS_type no
## sigma^2.5 0.5001 0.7072 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 9903.9628, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.2381, p-val = 0.6258
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1944 0.3914 -0.4967 510 0.6196 -0.9634 0.5746
## I(sqrt(1/N_tilde)) -0.2818 0.5776 -0.4879 510 0.6258 -1.4166 0.8530
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Full model with Ratio_liquid_fish taken as NA for the dry cooking category
egger_all0 <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(egger_all0)##
## Multivariate Meta-Analysis Model (k = 431; method: REML)
##
## logLik Deviance AIC BIC AICc
## -449.7851 899.5702 927.5702 984.2002 928.6021
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1247 0.3531 7 no Study_ID no
## sigma^2.2 0.0000 0.0001 26 no Phylogeny yes
## sigma^2.3 0.1680 0.4099 26 no Species_common no
## sigma^2.4 0.1389 0.3728 17 no PFAS_type no
## sigma^2.5 0.3591 0.5992 431 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 422) = 5075.0925, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## F(df1 = 9, df2 = 422) = 10.9795, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_CategoryNo liquid -2.2402 0.4370 -5.1260 422 <.0001
## Cooking_Categoryoil-based -0.6106 0.3634 -1.6804 422 0.0936
## Cooking_Categorywater-based -0.3507 0.3729 -0.9404 422 0.3476
## I(sqrt(1/N_tilde)) -0.1565 0.5497 -0.2847 422 0.7760
## scale(Publication_year) 0.3621 0.1268 2.8555 422 0.0045
## scale(Temperature_in_Celsius) 0.0078 0.0967 0.0810 422 0.9355
## scale(Length_cooking_time_in_s) -0.3697 0.0489 -7.5532 422 <.0001
## scale(PFAS_carbon_chain) 0.0756 0.0816 0.9256 422 0.3552
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8450 0.1355 -6.2354 422 <.0001
## ci.lb ci.ub
## Cooking_CategoryNo liquid -3.0992 -1.3812 ***
## Cooking_Categoryoil-based -1.3250 0.1037 .
## Cooking_Categorywater-based -1.0836 0.3823
## I(sqrt(1/N_tilde)) -1.2369 0.9239
## scale(Publication_year) 0.1128 0.6113 **
## scale(Temperature_in_Celsius) -0.1822 0.1979
## scale(Length_cooking_time_in_s) -0.4659 -0.2735 ***
## scale(PFAS_carbon_chain) -0.0849 0.2360
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1113 -0.5786 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1save(egger_all, egger_all0, egger_n, file = here("Rdata", "egger_regressions.RData"))Publication year
pub_year <- run_model(dat, ~Publication_year)
summary(pub_year)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -622.0347 1244.0693 1258.0693 1287.7102 1258.2924
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5611 0.7491 10 no Study_ID no
## sigma^2.2 0.0000 0.0036 38 no Phylogeny yes
## sigma^2.3 0.2173 0.4661 39 no Species_common no
## sigma^2.4 0.0972 0.3118 18 no PFAS_type no
## sigma^2.5 0.4995 0.7068 512 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 10284.7794, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 1.2887, p-val = 0.2568
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -166.0609 145.9966 -1.1374 510 0.2559 -452.8896
## Publication_year 0.0822 0.0724 1.1352 510 0.2568 -0.0600
## ci.ub
## intrcpt 120.7679
## Publication_year 0.2244
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat, pub_year, dat$Publication_year, "Publication year")
##
Sensitivity analyses
Leave-one-out analyses
Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one of the study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.
dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies
VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}
# The output is a list so we need to summarise the coefficients of all the models performed
results.Leave1studyout<-as.data.frame(cbind(
sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
sapply(Leave1studyout, function(x) summary(x)$zval), # extract the z value from all models
sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models
colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models| Estimate | SE | zval | pval | ci.lb | ci.ub |
|---|---|---|---|---|---|
| -0.3334920 | 0.3054621 | -1.0917624 | 0.2754540 | -0.9336137 | 0.2666296 |
| -0.4053512 | 0.3079689 | -1.3162081 | 0.1887049 | -1.0104180 | 0.1997156 |
| -0.4129172 | 0.3400610 | -1.2142447 | 0.2254044 | -1.0815429 | 0.2557085 |
| 0.0231863 | 0.2647027 | 0.0875939 | 0.9302356 | -0.4969193 | 0.5432919 |
| -0.3351006 | 0.3110170 | -1.0774349 | 0.2818283 | -0.9462269 | 0.2760257 |
| -0.2468997 | 0.2991220 | -0.8254148 | 0.4095403 | -0.8346286 | 0.3408292 |
| -0.3396240 | 0.3092942 | -1.0980614 | 0.2727083 | -0.9473063 | 0.2680583 |
| -0.2255378 | 0.3066928 | -0.7353867 | 0.4626444 | -0.8289340 | 0.3778583 |
| -0.3946729 | 0.3161633 | -1.2483198 | 0.2125200 | -1.0159016 | 0.2265557 |
| -0.4850959 | 0.2864141 | -1.6936873 | 0.0910091 | -1.0479545 | 0.0777626 |
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. ## # A tibble: 10 x 3
## # Groups: Author_year [10]
## Author_year Study_ID mean
## <chr> <fct> <dbl>
## 1 Alves_2017 F001 -0.0774
## 2 Barbosa_2018 F002 0.198
## 3 Bhavsar_2014 F003 0.153
## 4 DelGobbo_2008 F005 -2.00
## 5 Hu_2020 F006 -0.134
## 6 Kim_2020 F007 -0.887
## 7 Luo_2019 F008 -0.161
## 8 Sungur_2019 F010 -0.893
## 9 Taylor_2019 F011 0.213
## 10 Vassiliadou_2015 F013 0.673Subset analysis without Study_ID F005 (Del Gobbo et al. 2008)
Cooking time
dat.sens <- filter(dat, Author_year != "DelGobbo_2008")
include <- row.names(cor_tree) %in% dat.sens$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens <- cor_tree[include, include] # Only include the species that match the reduced data set
dat.sens <- as.data.frame(dat.sens) # convert data set into a data frame to calculate VCV matrix
VCV_lnRR.sens <- make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens <- rma.mv(lnRR, VCV_lnRR.sens, mods = ~Length_cooking_time_in_s, random = list(~1 |
Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
R = list(Phylogeny = cor_tree_sens), test = "t", data = dat.sens)
summary(mod.sens)##
## Multivariate Meta-Analysis Model (k = 430; method: REML)
##
## logLik Deviance AIC BIC AICc
## -261.8919 523.7837 537.7837 566.1976 538.0504
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1961 0.4428 8 no Study_ID no
## sigma^2.2 0.0220 0.1485 22 no Phylogeny yes
## sigma^2.3 0.0184 0.1357 22 no Species_common no
## sigma^2.4 0.0948 0.3079 17 no PFAS_type no
## sigma^2.5 0.0882 0.2969 430 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 428) = 2139.8639, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 428) = 106.1874, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.6361 0.2206 2.8837 428 0.0041 0.2026
## Length_cooking_time_in_s -0.0012 0.0001 -10.3047 428 <.0001 -0.0014
## ci.ub
## intrcpt 1.0697 **
## Length_cooking_time_in_s -0.0009 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dat.time.sens <- filter(dat.sens, Length_cooking_time_in_s != "NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s,
"Cooking time (s)") # The relationship with cooking time appears even stronger
Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")
oil_dat_time.sens <- filter(oil_dat.sens, Length_cooking_time_in_s != "NA")
water_dat_time.sens <- filter(water_dat.sens, Length_cooking_time_in_s != "NA")
dry_dat_time.sens <- filter(dry_dat.sens, Length_cooking_time_in_s != "NA")
model_oil_time.sens <- run_model(oil_dat_time.sens, ~Length_cooking_time_in_s)
model_water_time.sens <- run_model(water_dat_time.sens, ~Length_cooking_time_in_s)
model_dry_time.sens <- run_model(dry_dat_time.sens, ~Length_cooking_time_in_s)
summary(model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -123.3924 246.7848 260.7848 285.7364 261.2275
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2748 0.5242 5 no Study_ID no
## sigma^2.2 0.0000 0.0001 15 no Phylogeny yes
## sigma^2.3 0.0151 0.1230 15 no Species_common no
## sigma^2.4 0.1430 0.3781 16 no PFAS_type no
## sigma^2.5 0.0393 0.1982 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 261) = 750.9246, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 261) = 99.3161, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4430 0.2800 1.5817 261 0.1149 -0.1085
## Length_cooking_time_in_s -0.0015 0.0002 -9.9657 261 <.0001 -0.0018
## ci.ub
## intrcpt 0.9944
## Length_cooking_time_in_s -0.0012 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 120; method: REML)
##
## logLik Deviance AIC BIC AICc
## -100.9923 201.9845 215.9845 235.3793 217.0027
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1366 0.3696 7 no Study_ID no
## sigma^2.2 0.0122 0.1103 17 no Phylogeny yes
## sigma^2.3 0.0000 0.0001 17 no Species_common no
## sigma^2.4 0.0949 0.3081 15 no PFAS_type no
## sigma^2.5 0.1793 0.4235 120 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 118) = 1120.6077, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 22.6438, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.6846 0.2664 2.5695 118 0.0114 0.1570
## Length_cooking_time_in_s -0.0012 0.0002 -4.7586 118 <.0001 -0.0017
## ci.ub
## intrcpt 1.2123 *
## Length_cooking_time_in_s -0.0007 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_dry_time.sens)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.3971 22.7942 34.7942 45.6342 37.0047
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0000 0.0000 1 yes Study_ID no
## sigma^2.2 0.0082 0.0908 8 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 8 no Species_common no
## sigma^2.4 0.0727 0.2696 2 no PFAS_type no
## sigma^2.5 0.0233 0.1525 47 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 99.2612, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 38.7631, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.5035 0.2949 1.7071 45 0.0947 -0.0905
## Length_cooking_time_in_s -0.0014 0.0002 -6.2260 45 <.0001 -0.0019
## ci.ub
## intrcpt 1.0975 .
## Length_cooking_time_in_s -0.0010 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_time.sens <- predict.rma(model_oil_time.sens)
pred_water_time.sens <- predict.rma(model_water_time.sens)
pred_dry_time.sens <- predict.rma(model_dry_time.sens)
oil_dat_time.sens <- mutate(oil_dat_time.sens, ci.lb = pred_oil_time.sens$ci.lb,
ci.ub = pred_oil_time.sens$ci.ub, fit = pred_oil_time.sens$pred)
water_dat_time.sens <- mutate(water_dat_time.sens, ci.lb = pred_water_time.sens$ci.lb,
ci.ub = pred_water_time.sens$ci.ub, fit = pred_water_time.sens$pred)
dry_dat_time.sens <- mutate(dry_dat_time.sens, ci.lb = pred_dry_time.sens$ci.lb,
ci.ub = pred_dry_time.sens$ci.ub, fit = pred_dry_time.sens$pred)For some reason the plot doesn’t want to knit, although the script works
# Actual plot
ggplot(dat.sens, aes(x = Length_cooking_time_in_s, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_time.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = oil_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_time.sens, aes(y = fit), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_ribbon(data = dry_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.25) + geom_line(data = dry_dat_time.sens, aes(y = fit), size = 1.5,
col = "palegreen3") + col = "palegreen3") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "Cooking time (s)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model
##### Oil based
full_model_oil_time.sens<- run_model(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.6149 207.2299 227.2299 262.5242 228.1427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1966 0.4434 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0179 0.1336 15 no Species_common no
## sigma^2.4 0.1114 0.3337 16 no PFAS_type no
## sigma^2.5 0.0287 0.1694 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4897 0.2498 1.9608 252 0.0510 -0.0021
## scale(Temperature_in_Celsius) -0.0679 0.1352 -0.5017 252 0.6163 -0.3342
## Length_cooking_time_in_s -0.0015 0.0001 -10.2481 252 <.0001 -0.0018
## scale(PFAS_carbon_chain) 0.1421 0.0730 1.9471 252 0.0526 -0.0016
## scale(log(Ratio_liquid_fish)) -0.1155 0.2578 -0.4481 252 0.6545 -0.6233
## ci.ub
## intrcpt 0.9816 .
## scale(Temperature_in_Celsius) 0.1985
## Length_cooking_time_in_s -0.0012 ***
## scale(PFAS_carbon_chain) 0.2858 .
## scale(log(Ratio_liquid_fish)) 0.3922
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,oil_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time.sens<- run_model(water_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.4123 118.8246 136.8246 159.9970 138.8936
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2363 0.4861 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1177 0.3430 15 no PFAS_type no
## sigma^2.5 0.0670 0.2589 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 358.2103, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 16.7268, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.3049 0.3114 0.9793 97 0.3299 -0.3131
## Length_cooking_time_in_s -0.0013 0.0002 -6.1169 97 <.0001 -0.0018
## scale(PFAS_carbon_chain) 0.1667 0.0816 2.0422 97 0.0438 0.0047
## scale(log(Ratio_liquid_fish)) -0.3600 0.1581 -2.2774 97 0.0250 -0.6738
## ci.ub
## intrcpt 0.9229
## Length_cooking_time_in_s -0.0009 ***
## scale(PFAS_carbon_chain) 0.3287 *
## scale(log(Ratio_liquid_fish)) -0.0463 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(water_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time.sens<- run_model(dry_dat.sens, ~ Length_cooking_time_in_s)
pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")
ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Liquid volume to tissue sample ratio
Ratio_liquid_fish taken as NA for the dry cooking category
dat.sens.vol <- filter(dat.sens, Ratio_liquid_fish != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.vol <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.vol <- make_VCV_matrix(dat.sens.vol, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol <- rma.mv(lnRR, VCV_lnRR.sens.vol, mods = ~log(Ratio_liquid_fish), random = list(~1 |
Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
R = list(Phylogeny = cor_tree_sens.vol), test = "t", data = dat.sens.vol)
summary(mod.sens.vol)##
## Multivariate Meta-Analysis Model (k = 398; method: REML)
##
## logLik Deviance AIC BIC AICc
## -366.6269 733.2537 747.2537 775.1236 747.5424
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2829 0.5319 7 no Study_ID no
## sigma^2.2 0.0413 0.2033 26 no Phylogeny yes
## sigma^2.3 0.0902 0.3004 27 no Species_common no
## sigma^2.4 0.1203 0.3469 18 no PFAS_type no
## sigma^2.5 0.2299 0.4795 398 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 396) = 3669.5157, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 396) = 0.1265, p-val = 0.7223
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1160 0.2674 -0.4338 396 0.6647 -0.6416 0.4096
## log(Ratio_liquid_fish) -0.0144 0.0404 -0.3557 396 0.7223 -0.0937 0.0650
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat.sens.vol, mod.sens.vol, log(dat.sens.vol$Ratio_liquid_fish),
"ln(Liquid volume to tissue sample ratio (mL/g))") + scale_fill_manual(values = c("goldenrod2",
"dodgerblue3"))
Ratio_liquid_fish taken as 0 for the dry cooking category
dat.sens.vol0 <- filter(dat.sens, Ratio_liquid_fish_0 != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol0$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.vol0 <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.vol0 <- make_VCV_matrix(dat.sens.vol0, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol0 <- rma.mv(lnRR, VCV_lnRR.sens.vol0, mods = ~log(Ratio_liquid_fish_0 +
1), random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
~1 | Effect_ID), R = list(Phylogeny = cor_tree_sens.vol0), test = "t", data = dat.sens.vol0)
summary(mod.sens.vol0)##
## Multivariate Meta-Analysis Model (k = 467; method: REML)
##
## logLik Deviance AIC BIC AICc
## -423.5200 847.0400 861.0400 890.0343 861.2851
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2826 0.5316 7 no Study_ID no
## sigma^2.2 0.0401 0.2003 26 no Phylogeny yes
## sigma^2.3 0.1246 0.3530 27 no Species_common no
## sigma^2.4 0.1268 0.3561 18 no PFAS_type no
## sigma^2.5 0.2235 0.4728 467 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 465) = 4667.9738, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 465) = 2.5933, p-val = 0.1080
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.0390 0.2728 -0.1429 465 0.8864 -0.5751
## log(Ratio_liquid_fish_0 + 1) -0.0449 0.0279 -1.6104 465 0.1080 -0.0998
## ci.ub
## intrcpt 0.4971
## log(Ratio_liquid_fish_0 + 1) 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat.sens.vol0, mod.sens.vol0, log(dat.sens.vol0$Ratio_liquid_fish_0 +
1), "ln(Liquid volume to tissue sample ratio + 1) (mL/g)") + scale_fill_manual(values = c("#55C667FF",
"goldenrod2", "dodgerblue3"))
Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
oil_dat_vol.sens <- filter(oil_dat.sens, Ratio_liquid_fish != "NA")
water_dat_vol.sens <- filter(water_dat.sens, Ratio_liquid_fish != "NA")
model_oil_vol.sens <- run_model(oil_dat_vol.sens, ~log(Ratio_liquid_fish))
model_water_vol.sens <- run_model(water_dat_vol.sens, ~log(Ratio_liquid_fish))
summary(model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 297; method: REML)
##
## logLik Deviance AIC BIC AICc
## -287.7815 575.5631 589.5631 615.3719 589.9533
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4076 0.6384 6 no Study_ID no
## sigma^2.2 0.0616 0.2482 23 no Phylogeny yes
## sigma^2.3 0.0710 0.2665 24 no Species_common no
## sigma^2.4 0.0827 0.2875 17 no PFAS_type no
## sigma^2.5 0.2679 0.5176 297 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 295) = 2868.7819, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 0.0004, p-val = 0.9837
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.0831 0.3306 -0.2512 295 0.8018 -0.7338 0.5676
## log(Ratio_liquid_fish) 0.0009 0.0464 0.0204 295 0.9837 -0.0904 0.0923
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -78.5533 157.1066 171.1066 189.2725 172.3374
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6239 0.7899 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1361 0.3689 15 no PFAS_type no
## sigma^2.5 0.1172 0.3424 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 99) = 666.8997, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 99) = 8.9308, p-val = 0.0035
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.5789 0.4919 1.1769 99 0.2420 -0.3971 1.5549
## log(Ratio_liquid_fish) -0.4921 0.1647 -2.9884 99 0.0035 -0.8189 -0.1654
##
## intrcpt
## log(Ratio_liquid_fish) **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_vol.sens <- predict.rma(model_oil_vol.sens)
pred_water_vol.sens <- predict.rma(model_water_vol.sens)
oil_dat_vol.sens <- mutate(oil_dat_vol.sens, ci.lb = pred_oil_vol.sens$ci.lb, ci.ub = pred_oil_vol.sens$ci.ub,
fit = pred_oil_vol.sens$pred)
water_dat_vol.sens <- mutate(water_dat_vol.sens, ci.lb = pred_water_vol.sens$ci.lb,
ci.ub = pred_water_vol.sens$ci.ub, fit = pred_water_vol.sens$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_vol.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_vol.sens, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model
##### Oil based
full_model_oil_vol.sens <- run_model(oil_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
summary(full_model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.6149 207.2299 227.2299 262.5242 228.1427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1966 0.4434 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0179 0.1336 15 no Species_common no
## sigma^2.4 0.1114 0.3337 16 no PFAS_type no
## sigma^2.5 0.0287 0.1694 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.6350 0.2419 -2.6251 252 0.0092
## scale(Temperature_in_Celsius) -0.0679 0.1352 -0.5017 252 0.6163
## scale(Length_cooking_time_in_s) -0.3947 0.0385 -10.2481 252 <.0001
## scale(PFAS_carbon_chain) 0.1421 0.0730 1.9471 252 0.0526
## log_Ratio_liquid_fish -0.0354 0.0790 -0.4481 252 0.6545
## ci.lb ci.ub
## intrcpt -1.1114 -0.1586 **
## scale(Temperature_in_Celsius) -0.3342 0.1985
## scale(Length_cooking_time_in_s) -0.4706 -0.3189 ***
## scale(PFAS_carbon_chain) -0.0016 0.2858 .
## log_Ratio_liquid_fish -0.1909 0.1201
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_vol.sens <- predict.rma(full_model_oil_vol.sens, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat.sens$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol.sens <- as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens <- pred_oil_vol.sens %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol.sens <- run_model(water_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
summary(full_model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.4123 118.8246 136.8246 159.9970 138.8936
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2363 0.4861 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1177 0.3430 15 no PFAS_type no
## sigma^2.5 0.0670 0.2589 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 358.2103, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 16.7268, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.1442 0.3690 0.3909 97 0.6967 -0.5881
## scale(Length_cooking_time_in_s) -0.4742 0.0775 -6.1169 97 <.0001 -0.6280
## scale(PFAS_carbon_chain) 0.1667 0.0816 2.0422 97 0.0438 0.0047
## log_Ratio_liquid_fish -0.3135 0.1377 -2.2774 97 0.0250 -0.5868
## ci.ub
## intrcpt 0.8766
## scale(Length_cooking_time_in_s) -0.3203 ***
## scale(PFAS_carbon_chain) 0.3287 *
## log_Ratio_liquid_fish -0.0403 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_water_vol.sens <- predict.rma(full_model_water_vol.sens, addx = TRUE, newmods = cbind(0,
0, water_dat.sens$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol.sens <- as.data.frame(pred_water_vol.sens)
pred_water_vol.sens <- pred_water_vol.sens %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol.sens, aes(y = pred), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol.sens, aes(y = pred), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2)) #### The line doesn't go all the way down (the predict function doesn't capture the biggest values)PFAS carbon chain
dat.sens.PFAS <- filter(dat.sens, PFAS_carbon_chain != "NA")
include <- row.names(cor_tree) %in% dat.sens.PFAS$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.PFAS <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.PFAS <- make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.PFAS <- rma.mv(lnRR, VCV_lnRR.sens.PFAS, mods = ~PFAS_carbon_chain, random = list(~1 |
Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
R = list(Phylogeny = cor_tree_sens.PFAS), test = "t", data = dat.sens.PFAS)
summary(mod.sens.PFAS)##
## Multivariate Meta-Analysis Model (k = 486; method: REML)
##
## logLik Deviance AIC BIC AICc
## -450.3575 900.7149 914.7149 943.9895 914.9502
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2386 0.4885 9 no Study_ID no
## sigma^2.2 0.0889 0.2981 30 no Phylogeny yes
## sigma^2.3 0.1030 0.3209 31 no Species_common no
## sigma^2.4 0.0893 0.2988 18 no PFAS_type no
## sigma^2.5 0.2409 0.4908 486 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 484) = 5630.9463, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 484) = 1.3331, p-val = 0.2488
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.2514 0.3574 -0.7034 484 0.4822 -0.9536 0.4509
## PFAS_carbon_chain 0.0308 0.0267 1.1546 484 0.2488 -0.0216 0.0831
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length") # The relationship with cooking time appears even stronger
Effect of carbon chain length on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")
oil_dat_PFAS.sens <- filter(oil_dat.sens, PFAS_carbon_chain != "NA")
water_dat_PFAS.sens <- filter(water_dat.sens, PFAS_carbon_chain != "NA")
dry_dat_PFAS.sens <- filter(dry_dat.sens, PFAS_carbon_chain != "NA")
model_oil_PFAS.sens <- run_model(oil_dat_PFAS.sens, ~PFAS_carbon_chain)
model_water_PFAS.sens <- run_model(water_dat_PFAS.sens, ~PFAS_carbon_chain)
model_dry_PFAS.sens <- run_model(dry_dat_PFAS.sens, ~PFAS_carbon_chain)
summary(model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 297; method: REML)
##
## logLik Deviance AIC BIC AICc
## -287.8422 575.6844 589.6844 615.4933 590.0747
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.3746 0.6121 6 no Study_ID no
## sigma^2.2 0.0693 0.2633 23 no Phylogeny yes
## sigma^2.3 0.0691 0.2630 24 no Species_common no
## sigma^2.4 0.0737 0.2715 17 no PFAS_type no
## sigma^2.5 0.2686 0.5182 297 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 295) = 3184.3101, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 1.4752, p-val = 0.2255
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3813 0.4094 -0.9313 295 0.3524 -1.1870 0.4244
## PFAS_carbon_chain 0.0341 0.0281 1.2146 295 0.2255 -0.0212 0.0894
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 120; method: REML)
##
## logLik Deviance AIC BIC AICc
## -110.0472 220.0944 234.0944 253.4892 235.1125
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1421 0.3770 7 no Study_ID no
## sigma^2.2 0.0787 0.2805 17 no Phylogeny yes
## sigma^2.3 0.0089 0.0941 17 no Species_common no
## sigma^2.4 0.0683 0.2614 15 no PFAS_type no
## sigma^2.5 0.2187 0.4677 120 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 118) = 1375.3875, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 2.1408, p-val = 0.1461
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.5628 0.3730 -1.5088 118 0.1340 -1.3015 0.1759
## PFAS_carbon_chain 0.0486 0.0332 1.4632 118 0.1461 -0.0172 0.1144
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model_dry_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 69; method: REML)
##
## logLik Deviance AIC BIC AICc
## -66.1997 132.3993 146.3993 161.8322 148.2976
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5588 0.7475 2 no Study_ID no
## sigma^2.2 0.1626 0.4032 13 no Phylogeny yes
## sigma^2.3 0.0299 0.1730 14 no Species_common no
## sigma^2.4 0.0272 0.1648 7 no PFAS_type no
## sigma^2.5 0.2675 0.5172 69 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 67) = 779.4985, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 67) = 5.5149, p-val = 0.0218
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -1.1955 0.8525 -1.4024 67 0.1654 -2.8971 0.5061
## PFAS_carbon_chain 0.1594 0.0679 2.3484 67 0.0218 0.0239 0.2949 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_PFAS.sens <- predict.rma(model_oil_PFAS.sens)
pred_water_PFAS.sens <- predict.rma(model_water_PFAS.sens)
pred_dry_PFAS.sens <- predict.rma(model_dry_PFAS.sens)
oil_dat_PFAS.sens <- mutate(oil_dat_PFAS.sens, ci.lb = pred_oil_PFAS.sens$ci.lb,
ci.ub = pred_oil_PFAS.sens$ci.ub, fit = pred_oil_PFAS.sens$pred)
water_dat_PFAS.sens <- mutate(water_dat_PFAS.sens, ci.lb = pred_water_PFAS.sens$ci.lb,
ci.ub = pred_water_PFAS.sens$ci.ub, fit = pred_water_PFAS.sens$pred)
dry_dat_PFAS.sens <- mutate(dry_dat_PFAS.sens, ci.lb = pred_dry_PFAS.sens$ci.lb,
ci.ub = pred_dry_PFAS.sens$ci.ub, fit = pred_dry_PFAS.sens$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = dry_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = dry_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "palegreen3") + col = "palegreen3") +
geom_ribbon(data = oil_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_ribbon(data = water_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = water_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))Predictions with the full model
##### Oil based
full_model_oil_PFAS.sens<- run_model(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.6149 207.2299 227.2299 262.5242 228.1427
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1966 0.4434 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0179 0.1336 15 no Species_common no
## sigma^2.4 0.1114 0.3337 16 no PFAS_type no
## sigma^2.5 0.0287 0.1694 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.1519 0.3612 -3.1889 252 0.0016
## scale(Temperature_in_Celsius) -0.0679 0.1352 -0.5017 252 0.6163
## scale(Length_cooking_time_in_s) -0.3947 0.0385 -10.2481 252 <.0001
## PFAS_carbon_chain 0.0575 0.0296 1.9471 252 0.0526
## scale(log(Ratio_liquid_fish)) -0.1155 0.2578 -0.4481 252 0.6545
## ci.lb ci.ub
## intrcpt -1.8633 -0.4405 **
## scale(Temperature_in_Celsius) -0.3342 0.1985
## scale(Length_cooking_time_in_s) -0.4706 -0.3189 ***
## PFAS_carbon_chain -0.0007 0.1157 .
## scale(log(Ratio_liquid_fish)) -0.6233 0.3922
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, oil_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS.sens<- run_model(water_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.4123 118.8246 136.8246 159.9970 138.8936
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2363 0.4861 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1177 0.3430 15 no PFAS_type no
## sigma^2.5 0.0670 0.2589 101 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 358.2103, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 16.7268, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -1.2711 0.4044 -3.1433 97 0.0022 -2.0737
## scale(Length_cooking_time_in_s) -0.4742 0.0775 -6.1169 97 <.0001 -0.6280
## PFAS_carbon_chain 0.0736 0.0360 2.0422 97 0.0438 0.0021
## scale(log(Ratio_liquid_fish)) -0.3600 0.1581 -2.2774 97 0.0250 -0.6738
## ci.ub
## intrcpt -0.4685 **
## scale(Length_cooking_time_in_s) -0.3203 ***
## PFAS_carbon_chain 0.1451 *
## scale(log(Ratio_liquid_fish)) -0.0463 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, water_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS.sens<- run_model(dry_dat.sens, ~ PFAS_carbon_chain)
pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")
ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
Funnel plot
full_mod.sens <- run_model(dat.sens, ~-1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
funnel(full_mod.sens, yaxis = "seinv")
Figures for publication
Figure 2
Cooking time
full_model_time<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Liquid volume to tissue sample ratio
full_model_vol<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Ratio_liquid_fish=pred_full_model_vol$X.log_Ratio_liquid_fish
pred_full_model_vol<- pred_full_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
uni_model_vol<- run_model(dat, ~ log_Ratio_liquid_fish)
pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Ratio_liquid_fish=pred_uni_model_vol$X.log_Ratio_liquid_fish
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
p_vol<-ggplot(dat,aes(x = log_Ratio_liquid_fish, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Cooking temperature
full_model_temp<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))PFAS carbon chain length
full_model_PFAS<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine plots and save
(p_time + p_vol)/(p_temp + p_PFAS) + plot_annotation(tag_levels = c("A", "B", "C",
"D"))
ggsave("fig/Fig_2.png", width = 15, height = 12, dpi = 1200)Figure 2, with Ratio_liquid_fish taken as 0 for the dry cooking method
Cooking time
full_model_time0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_time0<-predict.rma(full_model_time0, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time0<-as.data.frame(pred_full_model_time0)
pred_full_model_time0$Length_cooking_time_in_s=pred_full_model_time0$X.Length_cooking_time_in_s
pred_full_model_time0<-left_join(dat, pred_full_model_time0, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Liquid volume to tissue sample ratio
full_model_vol0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish0)
pred_full_model_vol0<-predict.rma(full_model_vol0, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish0))
pred_full_model_vol0<-as.data.frame(pred_full_model_vol0)
pred_full_model_vol0$log_Ratio_liquid_fish0=pred_full_model_vol0$X.log_Ratio_liquid_fish
pred_full_model_vol0<- pred_full_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0)-1, lnRR = 0)
uni_model_vol0<- run_model(dat, ~ log_Ratio_liquid_fish0)
pred_uni_model_vol0<-predict.rma(uni_model_vol0, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol0<-as.data.frame(pred_uni_model_vol0)
pred_uni_model_vol0$log_Ratio_liquid_fish0=pred_uni_model_vol0$X.log_Ratio_liquid_fish
pred_uni_model_vol0<- pred_uni_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0) -1, lnRR = 0)
p_vol0<-ggplot(dat,aes(x = log_Ratio_liquid_fish0, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol0,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio + 1) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Cooking temperature
full_model_temp0<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_temp0<-predict.rma(full_model_temp0, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp0<-as.data.frame(pred_full_model_temp0)
pred_full_model_temp0$Temperature_in_Celsius=pred_full_model_temp0$X.Temperature_in_Celsius
pred_full_model_temp0<-left_join(dat, pred_full_model_temp0, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))PFAS carbon chain length
full_model_PFAS0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_PFAS0<-predict.rma(full_model_PFAS0, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS0<-as.data.frame(pred_full_model_PFAS0)
pred_full_model_PFAS0$PFAS_carbon_chain=pred_full_model_PFAS0$X.PFAS_carbon_chain
pred_full_model_PFAS0<-left_join(dat, pred_full_model_PFAS0, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine plots and save
(p_time0 + p_vol0)/(p_temp0 + p_PFAS0) + plot_annotation(tag_levels = c("A", "B",
"C", "D"))
ggsave("fig/Fig_2_zero_ratio.png", width = 15, height = 12, dpi = 1200)Figure 3
Adapt orchard_plot function
my_orchard<-function (object, mod = "Int", xlab, N = "none",
alpha = 0.5, angle = 90, cb = FALSE, transfm = c("none",
"tanh"), condition.lab = "Condition")
{
transfm <- match.arg(transfm)
if (any(class(object) %in% c("rma.mv", "rma"))) {
if (mod != "Int") {
object <- mod_results(object, mod)
}
else {
object <- mod_results(object, mod = "Int")
}
}
mod_table <- object$mod_table
data <- object$data
data$moderator <- factor(data$moderator, levels = mod_table$name,
labels = mod_table$name)
data$scale <- (1/sqrt(data[, "vi"]))
legend <- "Precision (1/SE)"
if (any(N != "none")) {
data$scale <- N
legend <- "Sample Size (N)"
}
if (transfm == "tanh") {
cols <- sapply(mod_table, is.numeric)
mod_table[, cols] <- Zr_to_r(mod_table[, cols])
data$yi <- Zr_to_r(data$yi)
label <- xlab
}
else {
label <- xlab
}
mod_table$K <- as.vector(by(data, data[, "moderator"],
function(x) length(x[, "yi"])))
group_no <- length(unique(mod_table[, "name"]))
cbpl <- c("#55C667FF", "goldenrod2", "dodgerblue3") # change colors
if (names(mod_table)[2] == "condition") {
condition_no <- length(unique(mod_table[, "condition"]))
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(y = yi, x = moderator, size = scale,
color = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerPR, ymax = upperPR), size = 0.75, # change size confidence intervals and swap CL with PR. Added whiskers
position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerCL, ymax = upperCL, shape = as.factor(condition), # swap CL with PR
fill = name), size = 1.6, stroke=2.2, width= 1.3, position = ggplot2::position_dodge2(width = 0.3)) + # change size point and prediction intervals
ggplot2::scale_shape_manual(values = 20 + (1:condition_no)) +
ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(0,
1), legend.justification = c(0, 1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend) +
ggplot2::labs(shape = condition.lab) + ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
else {
plot <- ggplot2::ggplot(data = mod_table, ggplot2::aes(x = estimate,
y = name)) + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(x = yi, y = moderator, size = scale,
colour = moderator), groupOnX = FALSE, alpha = alpha) +
ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerPR,
xmax = upperPR), height = 0, show.legend = FALSE, # change error barrs
size = 0.75, alpha = 0.5) + ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerCL,
xmax = upperCL), height = 0.1, show.legend = FALSE,
size = 1.75) + ggplot2::geom_vline(xintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_point(ggplot2::aes(fill = name), size = 8, # change point size
shape = 21) + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(1,
0), legend.justification = c(1, 0)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(x = label, y = "", size = legend) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
if (cb == TRUE) {
plot <- plot + ggplot2::scale_fill_manual(values = cbpl) +
ggplot2::scale_colour_manual(values = cbpl)
}
return(plot)
}Run full models in original units
full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# full model with Ratio_liquid_fish taken as `0` for the dry cooking category
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# full model without the 'No liquid' data for figure 3B, when Ratio_liquid_fish
# is taken as `NA` for the dry cooking category
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)Models with Ratio_liquid_fish taken as NA for the dry cooking category
Figure 3A
Estimates at cooking times of 2, 10 and 25 min
time_mm <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)Figure 3B
Estimates at 0 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm <-marginal_means(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", at = list(log_Ratio_liquid_fish= c(-2.3, 2.3, 3.8)), by = "log_Ratio_liquid_fish")
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)Figure 3C
Estimates at cooking times of 2, 10 and 25 min
time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.12))Combine plots and save
((p_time_mm/p_volume_mm) | p_time_mm_cat) + plot_annotation(tag_levels = c("A", "B",
"C"))
ggsave("fig/Fig_3.png", width = 14, height = 10, dpi = 1200)Models with Ratio_liquid_fish taken as 0 for the dry cooking category
Figure 3A
Estimates at cooking times of 2, 10 and 25 min
time_mm0 <-marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm0<-my_orchard(time_mm0, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.25))+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431),
parse = TRUE, hjust = "right", size = 3.5)Figure 3B
Estimates at 0.1 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm0 <-marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(log_Ratio_liquid_fish0= c(0, 2.4, 3.8)), by = "log_Ratio_liquid_fish0")
p_volume_mm0<-my_orchard(volume_mm0, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio + 1)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431),
parse = TRUE, hjust = "right", size = 3.5)Figure 3C
Estimates at cooking times of 2, 10 and 25 min
In this case, water- and oil-based cooking must be separated from dry cooking to avoid extrapolations of the dry cooking effect sizes at the mean liquid ratio.
time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_wat_oil<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 0), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = "none")+
guides(shape=F, size=F)+
ylim(-6.1, 3)+
annotate("text", y = 1.9, x = (seq(1, 2, 1) +
0.3), label = paste("italic(k)==", c(263, 121)),
parse = TRUE, hjust = "right", size = 3.5)
time_mm_dry<-marginal_means(full_model_org_units_dry, data = dat, at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_dry<-my_orchard(time_mm_dry, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("#55C667FF"))+
scale_colour_manual(values = c("#55C667FF"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = c(0.02,0.14),
legend.margin=margin(1,1,1,1))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 47),
parse = TRUE, hjust = "right", size = 3.5)
p_time_mm_cat<-p_time_mm_wat_oil/p_time_mm_dry + plot_layout(heights=c(2,1))Combine plots and save
((p_time_mm0/p_volume_mm0) | p_time_mm_cat) + plot_annotation(tag_levels = c("A",
"B", "C"))
ggsave("fig/Fig_3_zero_ratio.png", width = 14, height = 11, dpi = 1200)Figure 4, with Ratio_liquid_fish taken as NA for the dry cooking category
Figure 4A
##### Oil based
full_model_oil_time<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)Figure 4B
##### Oil based
full_model_oil_vol <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
p_4B <- ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full modelFigure 4C
full_model_oil_temp<- run_model(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")
p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)Figure 4D
##### Oil based
full_model_oil_PFAS<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine and save
(p_4A + p_4B)/(p_4C + p_4D) + plot_annotation(tag_levels = c("A", "B", "C", "D"))
ggsave("fig/Fig_4.png", width = 15, height = 12, dpi = 1200)Figure 4, with Ratio_liquid_fish taken as 0 for the dry cooking category
Figure 4A
##### Oil based
full_model_oil_time0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)Figure 4B
##### Oil based
full_model_oil_vol0 <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_oil_vol0 <- predict.rma(full_model_oil_vol0, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol0 <- as.data.frame(pred_oil_vol0)
pred_oil_vol0 <- pred_oil_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol0 <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_water_vol0 <- predict.rma(full_model_water_vol0, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol0 <- as.data.frame(pred_water_vol0)
pred_water_vol0 <- pred_water_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "water-based",
lnRR = 0)
p_4B0 <- ggplot(dat, aes(x = log(Ratio_liquid_fish_0 + 1), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol0, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol0, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio + 1) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2))Figure 4C
full_model_oil_temp0<- run_model(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_temp0<-predict.rma(full_model_oil_temp0, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp0<-as.data.frame(pred_oil_temp0)
pred_oil_temp0$Temperature_in_Celsius=pred_oil_temp0$X.Temperature_in_Celsius
pred_oil_temp0<-left_join(oil_dat, pred_oil_temp0, by="Temperature_in_Celsius")
p_4C0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)Figure 4D
##### Oil based
full_model_oil_PFAS0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))Combine and save
(p_4A0 + p_4B0)/(p_4C0 + p_4D0) + plot_annotation(tag_levels = c("A", "B", "C", "D"))
ggsave("fig/Fig_4_zero_ratio.png", width = 15, height = 12, dpi = 1200)Figure 5, with Ratio_liquid_fish taken as NA for the dry cooking category
Figure 5A
dat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f <- funnel(full_model,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)
p_5A <- recordPlot(plot_f)
invisible(dev.off())Figure 5B
full_model_egger <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)Figure 5C
full_model_pub <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")
p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))Combine and save
(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C) + plot_annotation(tag_levels = "A"))
ggsave(here("fig/Fig_5BC.png"), width = 18, height = 7, dpi = 1200)Figure 5, with Ratio_liquid_fish taken as 0 for the dry cooking category
Figure 5A
dat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f0 <- funnel(full_model0,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)
p_5A0 <- recordPlot(plot_f0)
invisible(dev.off())Figure 5B
full_model_egger0 <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_egger0<-predict.rma(full_model_egger0, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger0<-as.data.frame(pred_egger0)
pred_egger0$SE_eff_N=pred_egger0$X.I.sqrt.1.N_tilde..
pred_egger0<- pred_egger0 %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B0<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)Figure 5C
full_model_pub0 <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_pub0<-predict.rma(full_model_pub0, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub0<-as.data.frame(pred_pub0)
pred_pub0$Publication_year=pred_pub0$X.Publication_year
pred_pub0<-left_join(dat, pred_pub0, by="Publication_year")
p_5C0<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))Combine and save
(ggdraw(p_5A0) + ggdraw(p_5B0) + ggdraw(p_5C0) + plot_annotation(tag_levels = "A"))
ggsave(here("fig/Fig_5BC_zero_ratio.png"), width = 18, height = 7, dpi = 1200)Software and packages versions
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] cowplot_1.1.1 GGally_2.1.2 kableExtra_1.3.4
## [4] emmeans_1.6.2-9990002 patchwork_1.1.1 clubSandwich_0.5.3
## [7] ape_5.5 orchaRd_0.0.0.9000 metaAidR_0.0.0.9000
## [10] metafor_3.0-2 Matrix_1.3-4 here_1.0.1
## [13] googlesheets4_1.0.0 forcats_0.5.1 stringr_1.4.0
## [16] dplyr_1.0.7 purrr_0.3.4 readr_2.0.0
## [19] tidyr_1.1.3 tibble_3.1.3 ggplot2_3.3.5
## [22] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 googledrive_2.0.0 ggbeeswarm_0.6.0 colorspace_2.0-2
## [5] ellipsis_0.3.2 rprojroot_2.0.2 estimability_1.3 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.1.0 fansi_0.5.0 mvtnorm_1.1-2
## [13] lubridate_1.7.10 mathjaxr_1.4-0 xml2_1.3.2 codetools_0.2-18
## [17] splines_4.1.0 knitr_1.34 jsonlite_1.7.2 broom_0.7.9
## [21] dbplyr_2.1.1 compiler_4.1.0 httr_1.4.2 backports_1.2.1
## [25] assertthat_0.2.1 gargle_1.2.0 cli_3.0.1 formatR_1.11
## [29] htmltools_0.5.1.1 tools_4.1.0 coda_0.19-4 gtable_0.3.0
## [33] glue_1.4.2 Rcpp_1.0.7 cellranger_1.1.0 jquerylib_0.1.4
## [37] vctrs_0.3.8 svglite_2.0.0 nlme_3.1-152 xfun_0.24
## [41] rvest_1.0.1 lifecycle_1.0.0 MASS_7.3-54 zoo_1.8-9
## [45] scales_1.1.1 hms_1.1.0 parallel_4.1.0 sandwich_3.0-1
## [49] RColorBrewer_1.1-2 yaml_2.2.1 sass_0.4.0 reshape_0.8.8
## [53] stringi_1.7.3 highr_0.9 rlang_0.4.11 pkgconfig_2.0.3
## [57] systemfonts_1.0.2 evaluate_0.14 lattice_0.20-44 labeling_0.4.2
## [61] tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 bookdown_0.22
## [65] R6_2.5.1 generics_0.1.0 multcomp_1.4-17 DBI_1.1.1
## [69] pillar_1.6.2 haven_2.4.3 withr_2.4.2 survival_3.2-11
## [73] modelr_0.1.8 crayon_1.4.1 utf8_1.2.2 tzdb_0.1.2
## [77] rmarkdown_2.11 grid_4.1.0 readxl_1.3.1 rmdformats_1.0.2
## [81] reprex_2.0.1 digest_0.6.27 webshot_0.5.2 xtable_1.8-4
## [85] munsell_0.5.0 beeswarm_0.4.0 viridisLite_0.4.0 vipor_0.4.5
## [89] bslib_0.2.5.1